![82—Chapter 5. Strings and Dates
contains three elements, the expressions “Chicken Marsala”, “Beef Stew”, and “Hamburger”.
Notice how the escaped double quotes are used to group words into single list elements.
Interpreting a string as a list of elements allows us to make use of functions which operate
on each element in the string, rather than on each character. These methods can be useful
for string manipulation and pattern searching. For example, we may find the intersection,
union, or cross of two string lists. Additionally, we may manipulate the elements of a string
list and find the elements that match or do not match a given pattern. For example, the
string list function
@wkeep("ABC ABCC AABC", "?B*")
uses the pattern “?B*” to filter the string list “ABC ABCC AABC”. Elements with a single
character, followed by the character “B”, then followed by any number of other characters
are kept, returning: “ABC ABCC”.
String Functions
EViews provides a number of functions that may either be used with strings, or return string
values.
Functions that treat a string as a string list begin with a “w”. Some string functions have cor-
responding list functions with the same name, preceded by a “w”. For instance, @left
returns the leftmost characters of a string, while @wleft returns the leftmost elements of a
string list.
String Information Functions
The following is a brief summary of the basic functions that take strings as an argument and
return a number. (Chapter 17. “String and Date Function Reference,” on page 611 offers a
more detailed description.)
• @length(str): returns an integer value for the length of the string str.
@length("I did not do it")
returns the value 15.
A shortened keyword form of this function, @len, is also supported.
• @wcount(str_list): returns an integer value for the number of elements in the string
list str_list.
@wcount("I did not do it")
returns the value 5.
• @instr(str1, str2[, int]): finds the starting position of the target string str2 in the base
string str1. By default, the function returns the location of the first occurrence of str2
in str1. You may provide an optional integer int to specify the occurrence. If the
requested occurrence of the target string is not found, @instr will return a 0.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-89-320.jpg)
![Strings—83
The returned integer is often used in conjunction with @mid to extract a portion of the
original string.
@instr("1.23415", "34")
returns the value 4, since the substring “34” appears beginning in the fourth character
of the base string, so
@mid("1.23415", @instr("1.23415", "34"))
returns “3415”.
• @wfind(str_list, str_cmp): looks for the string str_cmp in the string list str_list, and
returns the element position in the list or 0 if the string is not in the list.
@wfind("I did it", "did")
returns the value 2.
The @wfindnc function performs the same operation, but the comparison is not case-
sensitive.
• @isempty(str): tests for whether str is a blank string, returning a 1 if str is a null
string, and 0 otherwise.
@isempty("1.23415")
returns a 0, while
@isempty("")
returns the value 1.
• @eqna(str1, str2): tests for equality of str1 and str2, treating null strings as ordinary
blank strings, and not as missing values. Strings which test as equal return a 1, and 0
otherwise. For example,
@eqna("abc", "abc")
returns a 1, while
@eqna("", "def")
returns a 0.
• @neqna(str1, str2): tests for inequality of str1 and str2, treating null strings as ordi-
nary blank strings, and not as missing values. Strings which test as not equal return a
1, and 0 otherwise.
@neqna("abc", "abc")
returns a 0,
@neqna("", "def")
returns a 1.
• @val(str[, fmt]): converts the string representation of a number, str, into a numeric
value. If the string has any non-digit characters, the returned value is an NA. You may](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-90-320.jpg)
![84—Chapter 5. Strings and Dates
provide an optional numeric format string fmt. See “String Conversion Functions” on
page 88 and @val (p. 638) for details.
• @dateval(str[, fmt]): converts the string representation of a date string, str, into a
date number using the optional format string fmt. See “String Conversion Functions”
on page 88 and @dateval (p. 618) for details.
• @dtoo(str): (Date TO Obs) converts the string representation of a date, str, into an
observation value for the active workfile. Returns the scalar offset from the beginning
of the workfile associated with the observation given by the date string. The string
must be a valid EViews date.
create d 2/1/90 12/31/95
%date = "1/1/93"
!t = @dtoo(%date)
returns the value !T=762.
Note that @dtoo will generate an error if used in a panel structured workfile.
String Manipulation Functions
The following is a brief summary of the basic functions that take strings as an argument and
return a string.
• @left(str, int): returns a string containing the int characters at the left end of the
string str. If there are fewer than int characters, @left will return the entire string.
@left("I did not do it", 5)
returns the string “I did”.
• @wleft(str_list, int): returns a string containing the int elements at the left end of the
string list str_list. If there are fewer than int elements, @wleft will return the entire
string list.
@wleft("I did not do it", 3)
returns the string “I did not”.
• @right(str, int): returns a string containing the int characters at the right end of a
string. If there are fewer than int characters, @right will return the entire string.
@right("I doubt that I did it", 8)
returns the string “I did it”.
• @wright(str_list, int): returns a string containing the int elements at the right end of
a string list. If there are fewer than int elements, @wright will return the entire string.
@wright("I doubt that I did it", 3)
returns the string “I did it”.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-91-320.jpg)
![Strings—85
• @mid(str, int1[, int2]): returns the string consisting of the characters starting from
position int1 in the string. By default, @mid returns the remainder of the string, but
you may specify the optional integer int2, indicating the number of characters to be
returned.
@mid("I doubt that I did it", 9, 10)
returns “that I did”.
@mid("I doubt that I did it", 9)
returns the string “that I did it”.
• @wmid(str_list, int1[, int2]): returns the string consisting of the elements starting from
position int1 in the string. By default, @wmid returns all remaining elements of the
string, but you may specify the optional integer int2, indicating the number of ele-
ments to be returned.
@wmid("I doubt that I did it", 2, 3)
returns “doubt you did”.
@mid("I doubt that I did it", 4)
returns the string “I did it”.
• @word(str_list, int): returns the int element of the string list.
@word("I doubt that I did it", 2)
returns the second element of the string, “doubt”.
• @wordq(str_list, int): returns the int element of the string list, while preserving
quotes.
@wordq("""Chicken Marsala"" ""Beef Stew""", 2)
returns the second element of the string, “Beef Stew”. The @word function would
return the same elements, but would not include quotation marks in the string.
• @insert(str1, str2, int): inserts the string str2 into the base string str1 at the position
given by the integer int.
@insert("I believe it can be done", "not ", 16)
returns “I believe it cannot be done”.
• @wkeep(str_list, "pattern_list"): returns the list of elements in str_list that match the
string pattern pattern_list. The pattern_list is space delimited, and may be made up of
any number of “?” (indicates any single character) or “*” (indicates any number of
characters).
@wkeep("ABC DEF GHI JKL", "?B? D?? *I")
keeps the first three elements of the string list, returning the string “ABC DEF GHI”.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-92-320.jpg)
![86—Chapter 5. Strings and Dates
• @wdrop(str_list, "pattern_list"): returns a string list, dropping elements in str_list that
match the string pattern pattern_list. The pattern_list is space delimited, and may be
made up of any number of “?” (indicates any single character) or “*” (indicates any
number of characters).
@wdrop("ABC DEF GHI JKL", "?B? D?? *I")
drops the first three elements of the string list, returning the string “JKL”.
• @replace(str1, str2, str3[, int]): returns the base string str1, with the replacement
str3 substituted for the target string str2. By default, all occurrences of str2 will be
replaced, but you may provide an optional integer int to specify the number of occur-
rences to be replaced.
@replace("Do you think that you can do it?", "you", "I")
returns the string “Do I think that I can do it?”, while
@replace("Do you think that you can do it?", "you", "I", 1)
returns “Do I think that you can do it?”.
• @wreplace(str_list, “src_pattern”, “replace_pattern”): returns the base string list
str_list, with the replacement pattern replace_pattern substituted for the target pattern
src_pattern. The pattern lists may be made up of any number of “?” (indicates any sin-
gle character) or “*” (indicates any number of characters).
@wreplace("ABC AB", "*B*", "*X*")
replaces all instances of “B” with “X”, returning the string “AXC AX”.
@wreplace("ABC DDBC", "??B?", "??X?")
replaces all instances of “B” which have two leading characters and one following
character, returning the string “ABC DDXC”.
• @ltrim(str): returns the string str with spaces trimmed from the left.
@ltrim(" I doubt that I did it. ")
returns “I doubt that I did it. ”. Note that the spaces on the right remain.
• @rtrim(str): returns the string str with spaces trimmed from the right.
@rtrim(" I doubt that I did it. ")
returns the string “ I doubt that I did it.”. Note that the spaces on the left remain.
• @trim(str): returns the string str with spaces trimmed from the both the left and the
right.
@trim(" I doubt that I did it. ")
returns the string “I doubt that I did it.”.
• @upper(str): returns the upper case representation of the string str.
@upper("I did not do it")](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-93-320.jpg)
![Strings—87
returns the string “I DID NOT DO IT”.
• @lower(str): returns the lower case representation of the string str.
@lower("I did not do it")
returns the string “i did not do it”.
• @addquotes(str): returns the string str with quotation marks added to the left and
right. Given a string S1 that contains the unquoted text: I did not do it,
@addquotes(S1)
returns the quoted string “I did not do it”.
• @stripquotes(str): returns the string str with quotation marks removed from the left
and right. Given a string S1 that contains the text: “I did not do it”,
@stripquotes(S1)
returns the unquoted string: “I did not do it”.
• @stripparens(str): returns the string str with parentheses removed from the left and
right. Given a string S1 that contains the text: “(I did not do it)”,
@stripparens(S1)
returns the string: “I did not do it”.
• @wintersect(str_list1, str_list2): returns the intersection of str_list1 and str_list2.
@wintersect("John and Greg won", "Mark won but Greg lost")
returns the string “won Greg”.
• @wunion(str_list1, str_list2): returns the union of str_list1 and str_list2.
@wunion("ABC DEF", "ABC G H def")
returns the string “ABC DEF G H def”. Each new element is added to the string list,
skipping elements that have already been added to the list.
• @wunique(str_list): returns str_list with duplicate elements removed from the list.
@wunique("fr1 fr2 fr1")
returns the string “fr1 fr2”.
• @wnotin(str_list1, str_list2): returns elements of str_list1 that are not in str_list2.
@wnotin("John and Greg won", "and Greg")
returns the string “John won”.
• @wcross(str_list1, str_list2[, “pattern”]): returns str_list1 crossed with str_list2,
according to the string pattern. The default pattern is “??”, which indicates that each
element of str_list1 should be crossed individually with each element of str_list2.
@wcross("ABC DEF", "1 2 3", "?-?")](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-94-320.jpg)
![88—Chapter 5. Strings and Dates
returns the string list “ABC-1 ABC-2 ABC-3 DEF-1 DEF-2 DEF-3”, inserting a dash (“-”)
between each crossed element as the “?-?” pattern indicates.
• @winterleave(str_list1, str_list2[, count1, count2]): Interleaves str_list1 with
str_list2, according to the pattern specified by count1 and count2. The default uses
counts of one.
@winterleave("A B C", "1 2 3")
interleaves “A B C” with “1 2 3” to produce the string list “A 1 B 2 C 3”.
• @wsort(str_list[,”D”]): Returns sorted elements of str_list. Use the “D” flag to sort in
descending order.
@wsort("fq8 Fq8 xpr1", "D")
sorts the string in descending order: “xpr1 Fq8 fq8”.
• @wdelim(str_list, "src_delim", "dest_delim"): returns a string list, replacing every
appearance of the src_delim delimiter in str_list with a dest_delim delimiter. Delimit-
ers must be single characters.
@wdelim("Arizona, California, Washington", ",", "-")
identifies the comma as the source delimiter and replaces each comma with a dash,
returning the string “Arizona-California-Washington”.
String Conversion Functions
The following functions convert between numbers or date numbers and strings:
• @datestr(date1[, fmt]): converts the date number date1 to a string representation
using the optional date format string, fmt.
@datestr(730088, "mm/dd/yy")
will return “12/1/99”,
@datestr(730088, "DD/mm/yyyy")
will return “01/12/1999”, and
@datestr(730088, "Month dd, yyyy")
will return “December 1, 1999”, and
@datestr(730088, "w")
will produce the string “3”, representing the weekday number for December 1, 1999.
See the function reference entry @datestr (p. 618) and “Dates” on page 94 for addi-
tional details on date numbers and date format strings.
• @dateval(str[, fmt]): converts the string representation of a date string, str, into a
date number using the optional format string fmt.
@dateval("12/1/1999", "mm/dd/yyyy")](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-95-320.jpg)
![Strings—89
will return the date number for December 1, 1999 (730088) while
@dateval("12/1/1999", "dd/mm/yyyy")
will return the date number for January 12, 1999 (729765). See the function reference
entry @dateval (p. 618) and “Dates,” beginning on page 94 for discussion of date
numbers and format strings.
• @str(num[, fmt]): returns a string representation of the number num. You may pro-
vide an optional numeric format string fmt.
@str(153.4)
returns the string “153.4”.
To create a string containing 4 significant digits and leading “$” character, use
@str(-15.4435, "g$.4")
The resulting string is “-$15.44”.
The expression
@str(-15.4435, "f7..2")
converts the numerical value, -15.4435, into a fixed 7 character wide decimal string
with 2 digits after the decimal and comma as decimal point. The resulting string is
“ -15,44”. Note that there is a leading space in front of the “-” character making the
string 7 characters long.
The expression
@str(-15.4435, "e(..2)")
converts the numerical value, -15.4435, into a string written in scientific notation with
two digits to the right of the decimal point. The decimal point in the value will be rep-
resented using a comma and negative numbers will be enclosed in parenthesis. The
resulting string is “(1,54e+01)”. A positive value will not have the parenthesis.
@str(15.4435, "p+.1")
converts the numeric value, 15.4435, into a percentage where the value is multiplied
by 100. Only 1 digit will be included after the decimal and an explicit “+” will always
be included for positive numbers. The resulting value after rounding is “+1544.4”.
See the function reference entry @str (p. 627) for a detailed description of the conver-
sion rules and syntax, along with additional examples.
• @val(str[, fmt]): converts the string representation of a number, str, into a numeric
value. If the string has any non-digit characters, the returned value is an NA. You may
provide an optional numeric format string fmt.
@val("1.23415")
See the function reference entry @val (p. 638) for a detailed description of the conver-
sion rules.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-96-320.jpg)
![Strings—91
• @strnow(fmt): returns a string representation of the current date number (at the
moment the function is evaluated) using the date format string, fmt.
@strnow("DD/mm/yyyy")
returns the date associated with the current time in string form with 2-digit days,
months, and 4-digit years separated by a slash, “24/12/2003”.
You may also ask EViews to report information about objects in the current workfile or data-
base, or a directory on your computer, in a form suitable for list processing:
• @wlookup(“pattern_list”[, “object_type_list”]): Returns a string list of all objects in the
workfile or database that satisfy the pattern_list and, optionally, the object_type_list.
The pattern_list may be made up of any number of “?” (indicates any single charac-
ter) or “*” (indicates any number of characters).
If a workfile contains a graph object named “GR01” and two series objects named
“SR1” and “SER2”, then
@wlookup("?R?","series")
returns the string “SR1”.
• @wdir(directory_str): returns a string list of all files in the directory directory_str. Note
that this does not include other directories nested within directory_str.
@wdir("C:Documents and Settings")
returns a string list containing the names of all files in the “C:Documents and Set-
tings” directory.
Lastly, all EViews objects have data members which return information about themselves in
the form of a string. For example:
ser1.@updatetime
returns the last update time for the series SER1
eq1.@command
returns the full command line form of the estimation command.
For lists of the relevant data members see the individual object descriptions in Chapter 1.
“Object View and Procedure Reference,” on page 2.
Using Strings in EViews
Strings in EViews are primarily used in four distinct contexts: string variables, string objects,
string vectors, or Alpha series.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-98-320.jpg)
![100—Chapter 5. Strings and Dates
• “f” or “F”: use frequency delimiter taken from the active, regular frequency workfile
page. The delimiter corresponds to the letter associated with the current workfile fre-
quency (“a”, “m”, “q”, ..., “A”, “M”, “Q”, ...), following the case of the format string,
or the colon (“:”), as determined by the Global Options setting (Options/General
Options.../Date representation).
• “?” : used as “wildcard” single character for skipping a character formats used on date
number input. Passed through to the output string.
• Other alphabetical characters are errors unless they are enclosed in square brackets
e.g. “[Q]”, in which case they are passed through to the output (for example, the
“standard-EViews” quarterly format is “YYYY[Q]Q”, where we use a four digit year
identifier, followed by a “Q” delimiter/identifier, followed by a single digit for the
quarter “1990Q2”).
• All other characters (e.g., punctuation) are passed through to the input or output
without special interpretation.
Translating between Date Strings and Date Numbers
There are times when it is convenient to work with date strings, and times when it is easier
to work with date numbers.
For example, when we are describing or viewing a specific date, it is easier to use a “human
readable” date string such as “2002-Mar-20”, “3/20/2002”, or “March 20, 2002 12:23 pm”
than the date number 730928.515972.
Alternatively, since date strings are merely text representations of dates, working with date
numbers is essential when manipulating calendar dates to find elapsed days, months or
years, or to find a specific date and time 31 days and 36 hours from now.
Accordingly, translating between string representations of dates and date numbers is one of
the more important tasks when performing advanced operations with dates in EViews.
These translations occur in many places in EViews, ranging from the interpretation of date
strings in sample processing, to the spreadsheet display of series containing date numbers,
to the import and export of data from foreign sources.
In most settings, the translations take place automatically, without user involvement. For
example, when you enter a sample command of the form
smpl 1990q1 2000q4
EViews automatically converts the date strings into a range of date numbers. Similarly,
when you edit a series that contains date numbers, you typically will enter your data in the
form of a date string such as
"2002-Mar-20"](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-107-320.jpg)
![Dates—105
Formatted Conversion
While the free-format conversions will generally produce the desired results, there may be
times when you want to exercise precise control over the conversion. EViews will perform a
formatted conversion between date strings and date numbers whenever you use the @dat-
eval or @datestr functions with the optional second argument specifying an explicit date
format.
To convert a date string into a date number using a date format, you should use the @dat-
eval function with two arguments. The first argument must be a valid date string, and the
second must be the corresponding date format string. If you omit the optional second argu-
ment, EViews will perform a free-format conversion.
• @dateval(str[, fmt]): takes the string str and evaluates it to a date number using the
optional date format string, fmt.
A few simple examples will illustrate the wide range of string to date number conversions
that are possible using @dateval and a date format. The simplest format strings involve the
standard month/day/year date descriptions:
@dateval("12/1/1999", "mm/dd/yyyy")
will return the date number for December 1, 1999 (730088),
@dateval("12/1/1999", "dd/mm/yyyy")
returns the date number for January 12, 1999 (729765). Here we have changed the interpre-
tation of the date string from “American” to “European” by reversing the order of the parts
of the format string.
Likewise, we may find the first date value associated with a given period
@dateval("1999", "yyyy")
returns the value 729754.0 corresponding to 12 midnight on January 1, 1999, the first date
value for the year 1999.
Conversion of an broad range of date strings is possible by putting together various date for-
mat string components. For example,
@dateval("January 12, 1999", "Month dd, yyyy")
returns the date number for 12 midnight on January 12, 1999 (729765), while
@dateval("99 January 12, 9:37 pm", "yy Month dd, hm:mi am")
yields the value 729765.900694 corresponding to the same date, but at 9:37 in the evening.
In this example, the “hm:mi” corresponds to hours (in a 12 hour format, with no leading
0’s) and minutes, and the “am” indicates that there is an indicator for “am” and “pm”. See
“Date Strings” on page 95 and “Date Formats” on page 97 for additional details.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-112-320.jpg)
![106—Chapter 5. Strings and Dates
To translate a date number to a date string using a date format, you should use the @dat-
estr function with two arguments. The first argument must be a valid date number, and the
second must be a date format string describing a string representation of the date.
• @datestr(date_val[, fmt]): converts the date number into a string, using the optional
date format fmt. If a format is not provided, EViews will use a default method (see
“Converting Date Numbers to Strings” on page 104).
For example,
@datestr(730088,"mm/dd/yy")
will return “12/1/99”,
@datestr(730088,"DD/mm/yyyy")
will return “01/12/1999”, and
@datestr(730088,"Month dd, yyyy")
will return “December 1, 1999”, and
@datestr(730088,"w")
will produce the string “3”, representing the weekday number for December 1, 1999. See
“Date Numbers” on page 96 and “Date Formats” on page 97 for additional details.
Translating Ordinary Numbers into Date Numbers
While date information is most commonly held in the form of date strings or date numbers,
one will occasionally encounter data in which a date is encoded as a (non-EViews format)
numeric value or values. For example, the first quarter of 1991 may be given the numeric
representation of 1991.1, or the date “August 15, 2001” may be held in the single number
8152001, or in three numeric values 8, 15, and 2001.
The @makedate function is used to translate ordinary numbers into date numbers. It is sim-
ilar to @dateval but is designed for cases in which your dates are encoded in one or more
numeric values instead of date strings:
• @makedate(arg1[, arg2[,arg3]], fmt): takes the numeric values given by the argu-
ments arg1, and optionally, arg2, etc. and returns a date number using the required
format string, fmt. Only a subset of all date formats are supported by @makedate.
If more than one argument is provided, the arguments must be listed from the lowest
frequency to the highest, with the first field representing either the year or the hour.
The simplest form of @makedate involves converting a single number into a date or a time.
The following are the supported formats for converting a single number into a date value:
• “yy” or “yyyy”: two or four-digit years.
• “yys” or “yyyys”: year*10 + half-year.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-113-320.jpg)
![110—Chapter 5. Strings and Dates
Note further that all of the relational operators are valid for comparing date numbers. Thus,
if two date numbers are equal, the “=”, “>=”, and “<=” relational operators all return a
1, while the “<>”, “>”, and “<” comparison operators return a 0. If two date numbers
are not equal, “<>” returns a 1 and “=” returns a 0. If the first date number is less than a
second date number, the corresponding first date precedes the second in calendar time.
Functions for Manipulating Dates
EViews provides several functions for manipulating dates that take date numbers as input
and return numeric values that are also date numbers. These functions may be used when
you wish to find a new date value associated with a given date number, for example, a date
number 3 months before or 37 weeks after a given date and time.
The functions described below all take a time unit string as an argument. As the name sug-
gests, a time unit string is a character representation for a unit of time, such as a month or a
year. The valid time unit string values are: “A” or “Y” (annual), “S” (semi-annual), “Q”
(quarters), “MM” (months), “WW” (weeks), “DD” (days), “B” (business days), “HH”
(hours), “MI” (minutes), “SS” (seconds).
There are three primary functions for manipulating a date number:
• @dateadd(date1, offset[, u]): returns the date number given by date1 offset by offset
time units as specified by the time unit string u. If no time unit is specified, EViews
will use the workfile regular frequency, if available.
Suppose that the value of date1 is 730088.0 (midnight, December 1, 1999). Then we
can add and subtract 10 days from the date by using the functions
@dateadd(730088.0, 10, "dd")
@dateadd(730088.0, -10, "dd")
which return 730098.0 (December 11, 1999) and (730078.0) (November 21, 1999).
Note that these results could have been obtained by taking the original numeric value
plus or minus 10.
The @dateadd function allows for date offsets specified in various units of time. For
example, to add 5 weeks to the existing date, simply specify “W” or “WW” as the
time unit string, as in
@dateadd(730088.0, 5, "ww")
which returns 730123.0 (January 5, 2000).
• @datediff(date1, date2[, u]): returns the number of time units between date1 and
date2, as specified by the time unit string u. If no time unit is specified, EViews will
use the workfile regular frequency, if available.
Suppose that date1 is 730088.0 (December 1, 1999) and date2 is 729754.0 (January 1,
1999), then,](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-117-320.jpg)
![Dates—111
@datediff(730088.0, 729754.0, "dd")
returns 334 for the number of days between the two dates. Note that this is result is
simply the difference between the two numbers.
The @datediff function is more powerful in that it allows us to calculate differences
in various units of time. For example, the expressions
@datediff(730088.0, 729754.0, "mm")
@datediff(730088.0, 729754.0, "ww")
return 11 and 47 for the number of months and weeks between the dates.
• @datefloor(date1, u[, step]): finds the first possible date number in the given time
unit, as in the first possible date value in the current quarter, with an optional step
offset.
If step is omitted, the frequency will use a step of 1 so that by default, @datefloor
will find the beginning of the period defined by the time unit.
Suppose that date1 is 730110.5 (12 noon, December 23, 1999). Then the @datefloor
values
@datefloor(730110.5, "dd")
@datefloor(730110.5, "mm")
yield 730110.0 (midnight, December 23, 1999) and 730088.0 (midnight, December 1,
1999), since those are the first possible date values in the current day and month.
Note that the first value is simply the integer portion of the original date number, but
that the latter required more complex analysis of the calendar.
Likewise, we can find the start of any corresponding period by using different time
units:
@datefloor(730098.5, "q")
@datefloor(730110.5, "y", 1)
returns 730027.0 (midnight, October 1, 1999), and 729754.0 (midnight, January 1,
1999. Notice that since the latter example used an offset value of 1, the result corre-
sponds to the first date value for the year 1999, which is the start of the year following
the actual value.
Extracting Information from Date Numbers
Given a date number you may wish to extract numeric values associated with a portion of
the value. For example, you might wish to know the value of the month, the year, or the day
in the year associated with a given date value. EViews provides the @datepart function to
allow you to extract the desired information.
• @datepart(date1, u): returns a numeric part of a date value given by u, where u is a
time unit string.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-118-320.jpg)
![114—Chapter 5. Strings and Dates
Month in year
• “mon/yy”
• “mon/yyyy”
• “yy/mon”
• “yyyy/mon”
The results are rounded to the first instance in the month (12 midnight of the first day of the
month).
Period in year
• “yyyy[S|Q|M|W|B|D|T|F|:]period”
• “yy[S|Q|M|W|B|D|T|F|:]period”
The date value is rounded to the first instance in the period in the year
Whole year
• “yyyy[A]”. The “A” is generally optional, but required if current WF is undated.
• “yy[A]”. The “A” is generally optional, but required if current WF is undated.
The date value is rounded to the first instance in the year (12 midnight on January 1).
Free-format Conversion Details
Note that the following conventions may be used in interpreting ambiguous free-form dates.
Dates and Date Intervals
A date in EViews is generally taken to represent a single point in calendar time. In some con-
texts, however, a date specification is used to refer to a range of values contained in a time,
which can be referred to as an interval.
When a date specification is treated as an interval, the precision with which the date is spec-
ified is used to determine the duration of the interval. For example, if a full day specification
is provided, such as “Oct 11 1980”, then the interval is taken to run from midnight at the
beginning of the day to just before midnight at the end of the day. If only a year is specified,
such as “1963”, then the interval is taken to run from midnight on the 1st of January of the
year to just before midnight on the 31st of December at the end of the year.
An example where this is used is in setting the sample for a workfile. In this context, pairs of
dates are provided to specify which observations in the workfile should be included in the
sample. The pairs of dates are provided are processed as intervals, and the sample is defined
to run from the start of the first interval to the end of the second interval. As an example, if](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-121-320.jpg)
![116—Chapter 5. Strings and Dates
1/3/91 7/5/95
so EViews will use the default date settings to determine the desired ordering. We caution
you, however, that using default settings to define the interpretation of date strings is not a
good idea since a given date string may be interpreted in different ways at different times if
your settings change. You may instead use the IEEE standard format, “YYYY-MM-DD” to
ensure consistent interpretation of your daily date strings. The presence of a dash in the for-
mat means that you must enclose the date in quotes for EViews to accept this format. For
example:
smpl "1991-01-03" "1995-07-05"
will always set the sample to run from January 3, 1991 and July 5, 1995.
Time of Day
Free-format dates can also contain optional trailing time of day information which must fol-
low the pattern:
hh[[[[[:mi:]ss].s]s]s][am|AM|pm|PM]
where “[]” encloses optional portions or the format and “|” indicates one of a number of
possibilities. In addition, either the “am” or “pm” field or an explicit minute field must be
provided for the input to be recognized as a time. An hour by itself is generally not suffi-
cient.
The time of day in an EViews date is accurate up to a particular millisecond within the day,
although any date can always be displayed at a lower precision. When displaying times at a
lower precision, the displayed times are always rounded down to the requested precision,
and never rounded up.
When both a day and a time of day are specified as part of a date, the two can generally be
provided one after the other with the two fields separated by one or more spaces. If, how-
ever, the date is being used in a context where EViews does not permit spaces between input
fields, a single letter “t” can also be used to separate the day and time so that the entire date
can be contained in a single word, e.g. “1990-Jan-03T09:53”.
Time Zones
There are a related set of functions that you may use that provide information on time-
zones: @localt (p. 622), @localt (p. 622), @tzlist (p. 635), @tzspec (p. 636), and
@utc (p. 637).](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-123-320.jpg)
![Program Modes—135
EViews provides you with the opportunity to set program execution modes at the time that
the program is first run. In addition, you may use the “MODE” statement to change the exe-
cution mode of a program from within the program itself. One important benefit to using
“MODE” statements is that the program can begin executing in one mode, and switch to a
second mode as the program executes.
To change the mode for quiet or verbose mode, simply add a line to your program reading
“MODE” followed by either the “QUIET” or the “VERBOSE” keyword, as in
mode quiet
For version 4 compatibility, you should use the keyword “VER4”:
mode ver4
as a line in the body of the program.
Multiple settings may be set in a single “MODE” line:
mode quiet ver4
and multiple mode statements may be specified in a program to change the mode as the pro-
gram runs:
mode quiet
[some program lines]
mode verbose
[additional program lines]
Note that setting the execution mode explicitly in a program overrides any settings specified
at the time the program is executed.
Program Message Logging
When executing a program in EViews, it may be useful to keep track of what is happening
during execution. Log windows allow you to determine more accurately the state of various
objects in your workfile or follow program progression.
Log windows may be switched on using the logmode (p. 407) command. One log window is
created for each program. If a program is executed more than once and a log window has
already been created, the log window will be cleared and all subsequent messages will be
sent to the existing log window. If you wish to preserve a log, you may either save the log to
a text file or freeze it, creating a text file object.
There are several types of messages which can be logged: program lines, status line mes-
sages, user log messages, and program errors. When displayed in a log message, each type
will appear in a different color, making it easier to differentiate one type from another. Pro-
gram lines are echoes of the line of code in the program currently being executed, and are
displayed in black. Status line messages are the messages displayed in the status line (see](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-142-320.jpg)
![140—Chapter 6. EViews Programming
else will be executed. If the condition is FALSE, all of the commands between else and
endif will be executed. For example:
if !scale>0 then
series newage = age/!scale
else
series newage = age
endif
(It is worth noting that this example performs a conditional recode in which the series NEW-
AGE is assigned using one expression if a condition is true, and a different expression other-
wise. EViews provides a built-in @recode function for performing this type of evaluation;
see @recode(s,x,y) (p. 550).)
IF statements may also be applied to string variables:
if %0="CA" or %0="IN" then
series stateid = 1
else
if %0="MA" then
series stateid=2
else
if %0="IN" then
series stateid=3
endif
endif
endif
Note that the nesting of our comparisons does not cause any difficulties.
You should note when using the IF statement with series or matrix objects that the compari-
son is defined on the entire object and will evaluate to false unless every element of the ele-
ment-wise comparison is true. Thus, if X and Y are series, the IF statement
if x<>y then
[some program lines]
endif
evaluates to false if any element of X does not equal the corresponding value of Y in the
default sample. If X and Y are identically sized vectors or matrices, the comparison is over
each of the elements X and Y. This element-wise comparison is described in greater detail in
“Relational Operators (=, >, >=, <, <=, <>)” on page 271.
If you wish to operate on individual elements of a series on the basis of element-wise condi-
tions, you should use the @recode function or use smpl statements to set the observations](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-147-320.jpg)
![146—Chapter 6. EViews Programming
Unlike a FOR statement, the WHILE statement does not update the control variable used in
the test condition. You need to include an explicit statement inside the loop to change the
control variable, or your loop will never terminate. Use the F1 key to break out of a program
which is in an infinite loop.
Earlier, we cautioned against this behavior creating FOR loops that explicitly change the
control variable inside the loop and offered an example to show the resulting lack of clarity
(p. 142). Note that the equivalent WHILE loop provides a much clearer program:
!i = 1
while !i<=25
vector a{!i}
!i = !i + 11
wend
Note that you may use the exitloop command to exit a WHILE loop early. See “Exiting
Loops” on page 147.
Execution Errors
By default, EViews will stop executing after encountering any errors. You can instruct the
program to continue running even if errors are encountered by changing the maximum error
count from the Run dialog (see “Executing a Program” on page 120), or by using the set-
maxerrs (p. 721) command inside a program.
Handling Errors
You may wish to perform different tasks when errors are encountered. For example, you may
wish to skip a set of lines which accumulate estimation results when the estimation proce-
dure generated errors, or you may wish to overwrite the default EViews error with one of
your own, using the seterr (p. 720) command.
EViews offers a number of different ways to test for and handle execution errors. For exam-
ple, the @lasterrstr (p. 715) command will return a string containing the previous line's
error. If the previous line of your program did not error, this string will be empty. Alterna-
tively you could use the @errorcount (p. 709) function to check the number of errors cur-
rently encountered before and after a program line has been executed.
For example, to test whether the estimation of an equation generated an error, you can com-
pare the number of errors before and after the command:
!old_count = @errorcount
equation eq1.ls y x c
!new_count = @errorcount
if !new_count > !old_count then
[various commands]](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-153-320.jpg)
![Subroutines—149
at the top of any other program that needs to use the commands in POWERS. include also
accepts a full path to the program file, and you may have more than one include statement
in a program. For example, the lines,
include c:programspowers.prg
include durbin_h
[more lines]
will first execute all of the commands in “C:ProgramsPowers.PRG”, will execute the com-
mands in “Durbin_h.PRG”, and then will execute the remaining lines in the program file.
If you do not provide an absolute path for your include file, EViews will use the location of
the executing program file as the base location. In the example above, EViews will search for
the “Durbin_h.PRG” file in the directory containing the executing program.
Note that in contrast to using exec to execute another program, include simply inserts the
child program into the parent. This insertion is done prior to executing any lines in either
program. One important consequence of this behavior is that any program variables that are
declared in the parent program will not be available in the child/included program, even if
they are declared prior to the include statement.
Subroutines
A subroutine is a collection of commands that allows you to perform a given task repeatedly,
with minor variations, without actually duplicating the commands. You may also use sub-
routines from one program to perform the same task in other programs.
Defining Subroutines
A subroutine begins with the keyword subroutine followed by the name of the routine and
any arguments, and ends with the keyword endsub. Any number of commands can appear
in between. The simplest type of subroutine has the following form:
subroutine z_square
series x = z^2
endsub
where the keyword subroutine is followed only by the name of the routine. This subrou-
tine has no arguments so that it will behave identically every time it is used. It forms the
square of the existing series Z and stores it in the new series X.
You may use the return command to force EViews to exit from the subroutine at any time.
A common use of return is to exit from the subroutine if an unanticipated error is detected.
The following program exits the subroutine if Durbin’s statistic (Greene, 2008, p. 646, or
Davidson and MacKinnon, 1993, p. 360) for testing serial correlation with a lagged depen-
dent variable cannot be computed:
h](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-156-320.jpg)
![User-Defined Dialogs—165
The return value of the control is determined by the user response: Cancel (-1) or OK (0).
See @uiradio (p. 734) for additional detail.
@uidialog
The @uidialog(control_spec1[, control_spec2, ….]) function displays a dialog which may be
composed of different controls, including simple text, edit boxes, list boxes, radio buttons
and check boxes. The dialog is specified using a list of control specifications passed into the
function as arguments. Each control specification is a type keyword specifying the type of
control, followed by a list of parameters for that control.
The type keywords should be from the following list:
The “edit”, “list” and “radio” controls are similar to their individual dialog functions, and
the specifications for those controls follow the same pattern. Thus the specification for an
edit box would be (“edit”, string IOString, string prompt, scalar maxEditLen).
The “caption” control changes the title of the dialog, shown in the title bar. The caption
keyword should be followed by a string containing the text to be used as the caption, yield-
ing a specification of (“caption”, string caption).
Keyword Control
“caption” Dialog title
“text” Plain text
“edit” Edit box
“list” List box
“radio” Radio buttons
“check” Check box
“button” OK-type button
“buttonc” Cancel-type button
“colbreak” Column break
“setoc” Set OK/Cancel text](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-172-320.jpg)
![166—Chapter 6. EViews Programming
The “text” control adds basic text to the dialog. Like the caption control, the text control
keyword, “text”, should be followed by a string containing the text to be used, yielding a
specification of (“text”, string text).
The “check box” control adds a check box to the dialog. The check keyword should be fol-
lowed by a scalar, IOScalar, which stores the selection of the check box - 1 for checked, and
0 for unchecked, and then by a string prompt which contains the text to be used as a
prompt/instructions for the check box. The specification for the check box control is then:
(“check”, scalar IOScalar, string prompt).
The “button” and “buttonc” controls add a custom button to the dialog. The dialog will
close after a button has been pressed. The behavior of the button will depend on the type of
button —buttons of type “button” will behave in the same way as the “OK” button (i.e., all
variables passed into the dialog will be updated to reflect changes made to their correspond-
ing controls). Buttons of type “buttonc” will behave in the same way as the “Cancel” button
(i.e., all variables will be reset to the values that were passed into the dialog).
The return value of the dialog will correspond to the order in which buttons are placed in
the dialog. If only one button (apart from the standard “OK” and “Cancel”) is included in
the dialog, the return value for that button will be “1”. If there is more than one button, then
the first button will return a value of “1”, the second will return a value of “2” and so on.
Note that the return value is independent of whether the button was of type “button” or
“buttonc”. The specification for the button controls is (“button[c]”, “text”) where text speci-
fies the text that will be on the button.
The column break control inserts a column break. By default, EViews will automatically
choose the number of columns in the constructed dialog. There is still a maximum of only
two columns allowed, but by adding a “colbreak” control, you can force the position of a
break in the dialog.
“setoc” allows you to change the text of the “OK” and “Cancel” buttons on the dialog. You
should supply two words, separated by a space as the text for “OK” and “Cancel”.
As an example, a dialog that offers a choice of covariance matrix options, plus a check box
for degree of freedom adjustment, could be made with:
!choice = 2
!doDF = 1
%list = "Default White HAC"
scalar ret = @uidialog("caption", "Covariance Options", "list",
!choice, "Covariance method", %list, "check", !doDF, "DF-
adjust")](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-173-320.jpg)
![180—Chapter 7. External Connectivity
Installing R Components
EViews was developed and tested with the following R components. You must have the fol-
lowing installed on the machine running EViews (or have R server components registered
on the same machine via DCOM):
• R, version 2.8.1 or later.
• rscproxy, version 1.0-12.
• statconnDCOM, version 3.1-1B2.
Newer versions of rscproxy or statconnDCOM may work, but have not been tested with
EViews. Note that rscproxy version 1.0-13 has known issues with the console output feature,
such that you may not see any output in the R Output Log window in EViews.
New R installation
If you do not currently have R installed, the easiest procedure is to run the latest RAnd-
Friends setup file located at
http://rcom.univie.ac.at/download.html
As of this documentation, RAndFriendsSetup2090V3.0-14-2.exe is the latest file, which
includes the proper rscproxy version 1.0-12. Run the basic installation of RAndFriendsSet-
up2090V3.0-14-2.exe, leaving the Direct internet connection box checked, to install R ver-
sion 2.9.0, rscproxy version 1.0-12, and statconnDCOM version 3.1-1B2. After installing
statconnDCOM, you may cancel out of the installation of RExcel, as it is not needed by
EViews.
Modifying an Existing R installation
If you already have R installed, verify that it is at least version 2.8.1. Also, you should check
your R installation write path and version information in your windows registry, as this
information is needed by statconnDCOM to find your R installation. You should have an
entry in the registry that looks like the following:
Windows Registry Editor Version 5.00
[HKEY_LOCAL_MACHINESOFTWARER-core]
[HKEY_LOCAL_MACHINESOFTWARER-coreR]
"InstallPath"="C:Program FilesRR-2.8.1"
"Current Version"="2.8.1"
[HKEY_LOCAL_MACHINESOFTWARER-coreR2.8.1]](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-187-320.jpg)
![Using Add-ins—191
Using Add-ins
Add-ins are integrated into the EViews menus and command line so that they work much
like built-in routines. To run an Add-in program, simply select the corresponding menu entry
or issue the appropriate command.
Beyond that, working with Add-ins menu and command entries does require some under-
standing of the difference between the two types of Add-ins: object-specific and global. As
the names suggest, object-specific Add-ins are designed to work with a single object type,
while global Add-ins are designed to work more generally with more than one object or
object type.
For example, an Add-in that computes a spline using data in a series is likely to be object-
specific, since it operates on a single series, while an Add-in that copies tables, graphs, and
spools into an RTF file would naturally be defined as global.
The menu entries and form of commands differs between the two Add-in types.
• Global Add-ins have menu entries that appear only in the main Add-ins menu. Global
add-in commands follow the EViews command syntax:
command(options) [args]](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-198-320.jpg)
![192—Chapter 8. Add-ins
• Object-specific Add-ins have menu entries that appear in both the main Add-ins menu
and in the menu of objects of the specified object type. Object-specific Add-in com-
mands follow the standard EViews object command syntax:
object_name.command(options) [args]
Suppose, for example, we have a global Generate time-series data Add-in with associated
command tsdgp. Since the Add-in is global, it will have a menu item in the main Add-ins
menu,
Moreover, the global command
tsdgp(diff="2", seed=100, meanconst="2", ar="0.1", ma="0.15",
varconst="0.8", arch = "0.15", garch="0.2 0.2") y
will run the Add-in program with the specified options.
Suppose, in addition, that we have two equation-specific Add-ins, Simple rolling regression
and Advanced rolling regression, with associated object-specific commands, roll and
advroll. If equation EQ1 is the active object, the main Add-ins menu will contain both the
global (Generate time series data) and the two equation-specific entries (Simple rolling
regression and Advanced rolling regression):](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-199-320.jpg)
![206—Chapter 8. Add-ins
automate the Add-in installation and allow for automatic updating of your Add-in as you
provide newer versions. We strongly recommend that package distributors take the time to
create these files as described below.
Table of Contents
First, you should create a table-of-contents file named “Toc.INI” for inclusion in your pack-
age. The TOC file should contain setup information which describes the directory in which
the Add-in files should be installed, and if appropriate, the name of an EViews installer pro-
gram for registering the Add-in. The format of the TOC file is:
[package]
installer = <name of installer file>
folder = <name of folder to create>
A TOC file should always begin with the line “[package]”, followed by lines which give the
directory and installer information.
The installer keyword is used to indicate the name of the EViews program file, if one
exists, that should be run to register the Add-in (see “Installer Program” on page 207). If, for
example, a registration program file named “Recession shade install.PRG” is included in
your package, you should include the line
installer = recession shade install.prg
If you include this line in your TOC, EViews will automatically run the installer program
when it opens the AIPZ package. If you do not wish to provide an installer program, you
should include the line “installer=none” in the TOC file.
The folder keyword may be used to indicate a subfolder of the default Add-ins directory
into which you will extract the packaged files. Thus,
folder = RecShade
tells EViews to extract the contents of the AIPZ file into the “RecShade” folder of the Add-ins
directory. If no folder is specified, the name of the AIPZ file will be used as the target folder
name. Additionally, you may use the special folder name “<addins>” to indicate that the
contents of the AIPZ archive should be placed in the main Add-ins directory. (Note, how-
ever, that only folders in an AIPZ archive may be written to the main Add-ins directory in
this fashion; individual files in AIPZ files must be written into subdirectories.
We emphasize that creating a TOC file and providing an installer program are not required.
In the absence of a TOC file or an installer= specification, EViews will, after unpacking
the AIPZ files, simply display a message reminding the user that the installed programs may
be registered manually using the Add-ins management dialog.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-213-320.jpg)
![210—Chapter 8. Add-ins
simply the opened series or group whose menu we are using (the last one active in the
workfile).
Alternately, if we had registered the program as a series-specific Add-in with proc name
“myl”, the command:
ser01.myl
identifies SER01 as the _this object, so that the object used by the Add-in will be the series
SER01, regardless of which object is active in the workfile.
Lastly, you may specify _this explicitly when using the exec or run command to run the
program by including the “this=” option to identify an object by name. The command:
exec(this=ser01) myline.prg
explicitly specifies SER01 as the _this object.
Custom Object Output
EViews allows you to display the contents of a table, graph, or spool object in the window of
another object. This feature will undoubtedly most often be used to mimic the behavior of
EViews views and procedures in Add-ins programs.
Suppose, for example, that your Add-in program performs some calculations and constructs
an output table TABLE_OUT. You may instruct EViews to display the contents of
TABLE_OUT in the object OBJECT_01 using the display command:
object_01.display table_out
Thus, a useful approach to constructing an object-specific Add-in involves creating a pro-
gram of the form:
[use _this to perform various calculations]
[create an output table or graph, say the table TABLE01]
’ put the output in the _this window
_this.display table01
delete table01
Note that we delete the TABLE01 after we put it in the window of _this. (You may instead
wish to employ local subroutine to enable automatic cleanup of the temporary table.)
If the above program is registered as a series-specific Add-in with the command “FOO”, then
you may run it by issuing the command
series01.foo
which will display the output in the SERIES01 window.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-217-320.jpg)
![Examples—219
We note that installing and running an EViews program file provided by an unknown indi-
vidual has risks. Accordingly, we recommend that care be taken when installing packages
from non-trusted sites.
All of the packages provided on the EViews website have been examined to ensure that they
do not include potentially harmful commands.
Working with Registered User Objects
Once you have registered your user object you may work with it much as you would any
built-in EViews object.
You can create a new instance of the object using the Object/New Object... main menu
item, or by declaring it on the command line using the name of the object and any relevant
options or arguments:
userobj_class_name(options) my_objname [args]
You may use the defined views and procs of the object using the object View or Proc menu,
or via the command line using the standard syntax:
userobj_name.view_name(options) [args]
userobj_name.proc_name(options) [args]
The user object data member @-functions may be accessed using the syntax:
[result_type] result = userobj_name.@datamember_name[(arg)]
Examples
To illustrate the use of registered user objects, we examine two of the EViews user objects
that are currently available for download from our website. (To follow along with these
examples, we recommend that you first download and install the corresponding user object
using the steps outlined in “Downloading a Registered User Object” on page 216.)
The first example uses a simple container user object (of type ResStore) that allows you to
add, extract, and display graphs, tables and estimation objects. The second example per-
forms rolling regression estimation using the Roll user object.
Simple Container Object (ResStore)
This first example uses the ResStore user object to create a storage container for objects from
our workfile. This is a bare bones registered object that nonetheless shows the basic features
of registered user objects.
We use the workfile “Demo.WF1” (which may be found in the example files subdirectory of
your EViews installation directory) and assume that you have already installed the ResStore
object class.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-226-320.jpg)
![Defining a Registered User Object Class—231
• Create an object definition file which includes constructor, view, and proc definitions.
• Register the object definition file with EViews.
This discussion assumes that you have already written programs to initialize your object and
possibly to display views and execute procs. These programs will be standard EViews pro-
grams that use ordinary EViews commands. There are, however, several programming fea-
tures which are designed specifically for user object (and Add-in) program development that
you should find useful. See “Add-ins Design Support” on page 209 in the Command and Pro-
gramming Reference.
Creating an Object Definition File
The object definition file is a simple text file with a “INI” extension. This file describes how
to construct the custom object using an EViews program and optionally provides menu
items and custom command names for views and procs that will execute EViews programs.
There are three sections in the file, corresponding to the constructor, the views, and the
procs of the object.
The first section of the definition file should start with a line consisting of the text “[con-
structor]” (without the quotes). The line immediately following should contain the path and
name of an EViews program file that will be used as the constructor. The constructor pro-
gram file describes how the object should be initialized, or constructed, when you create a
new instance using the Object/New Object... menu item or the command line.
The second section contains the view definition specifications. This section should start
with a line consisting of the keyword “[views]”. Each line following this keyword will define
a view for the user object. Each view definition line should consist of the menu text, fol-
lowed by a comma, a custom command name for the view, a comma, the path and name of
the program file to be run when the view is selected from the menu or run from the com-
mand line.
The third section contains the proc definition specifications. It follows the same format as
the views section, but begins with “[procs]” rather than “[views]”.
Note that when providing the path of the programs in your definitions, the “.” shortcut can
be used to denote the folder containing the definition INI file.
For example, the following is the definition file for the ResStore user object:
[constructor]
".resstore construct.prg"
[views]
"View stored objects", objects, ".viewall.prg"
"View stored equations", equations, ".viewequations.prg"](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-238-320.jpg)
![232—Chapter 9. User Objects
"View stored graphs", graphs, ".viewgraphs.prg"
"view stored tables", tables, ".viewtables.prg"
[procs]
"Extract objects from store", extractobjects, ".extract
objects.prg"
"Add objects to store", addobjects, ".resstore construct.prg"
"Remove objects from store", dropobjects, ".remove objects.prg"
The ResStore user object use a constructor program called “resstore construct.PRG” which is
called when you create a new ResStore object from the dialogs or command line.
There are four view menu items, each associated with a different EViews program. The first
view definition tells EViews that it should create a view menu item View stored objects and
object command objects, and associate both with the EViews program “viewall.PRG”
(which is located in the ResStore directory). This definition means that selecting View/View
stored objects from the object menu, or entering the object command
my_object.objects
will run the “viewall.PRG” program which displays all of the stored objects in MY_OBJECT.
Similarly, there are three proc menu items. Selecting Proc/Remove objects from store or
issuing the command
my_object.dropobjects
runs the “remove objects.PRG” program which displays a dialog prompting you for the
name of the objects to remove from MY_OBJECT.
Registering a User Object Class
To register a new user object class, select Add-ins/Manage User Objects... from the main
EViews menu to display the User objects management dialog, then click on the Add button
to display the Add/Edit Program dialog.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-239-320.jpg)
![234—Chapter 9. User Objects
tribution and, if you provide an installer program, offers automatic installation of the object
by dragging-and-dropping the package onto EViews or double clicking on the file.
The process of creating a user object package is virtually identical to packaging of an EViews
Add-in, with a few minor exceptions. We summarize briefly the main steps. Related discus-
sion may be found in.
Creating the Self-installing Package
The process of creating a user object package is straightforward, requiring only two steps:
• (optional) Create a table of contents (TOC) information file and installer program file
to allow for automatic registration and updating of the Add-in.
• Create a self-extracting user object package file containing the object definition file
and any support files (including the TOC and installer program file, if available).
To create the self-extractive file simply create a standard ZIP archive file containing all of the
files for your user object, then rename the ZIP file so that it has the extension “UOPZ”.
Opening such an file, automatically after completing the download, by double clicking on
the file, or by dropping it onto EViews, will begin the automatic installation procedure.
The user object will not, however be automatically installed and registered unless you
include a table of contents (TOC) information file and installer program along with your pro-
gram files. Creating the TOC and installer program files takes only a few minutes and allows
you to support automatic registration and updating of the user object. We strongly recom-
mend that package distributors take the time to create these files as described below.
Table of Contents
Next, you should create a table-of-contents file named “Toc.INI”. The TOC file should con-
tain setup information for the user object which describes the directory in which it should
be installed, and the name of the EViews program, if any, that should be run to register the
user object files. The format of the TOC file is:
[package]
installer = <name of installer file>
folder = <name of folder to create>
A TOC file should always begin with the line “[package]”. The “installer” keyword is used to
indicate the name of the EViews program file that should be run to register the user object.
If, for example, a registration file named “Roll install.PRG” is included in your package, you
should include the line
installer = roll install.prg
The “folder” keyword may be used to indicate the subfolder of the default user object direc-
tory into which you wish to extract the package files. Thus,](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-241-320.jpg)
![User Object Programming Support—237
To declare a registered user object, you will use the name of the class followed by the name
of the object:
userobj_class_name(options) myobject [args]
Depending on how the user object is designed, the declaration program may use options and
additional arguments args when running the constructor program.
See Userobj::userobj (p. 817).
Registration
The adduo command may be used to register a user object, as in
adduo(name="roll", version="1.0", desc="Rolling regression
object") ./rolldef.ini
You may use the command to specify the object definition file, path, description, version
number, documentation file, XML file, etc.
See adduo (p. 290) for details.
View and Procs
Each type of user object will have its own views and procs:
• All user objects provide a small number of generic built-in views and procs. For exam-
ple, the standard EViews label view for viewing and modifying the label contents
and the display view for showing output in the object window are supported.
• All user objects support the add and drop procs which may be used to populate the
user object, and the extract proc may be employed to extract objects into the work-
file.
• In addition, registered user objects may provide custom views and procs. You should
view the specific user object documentation file for details.
As with any EViews object, you may access the views and procs of the user object using the
object View or Proc menu, or via the command line using the standard syntax:
myobject.view_name(options) [args]
myobject.proc_name(options) [args]
See “User Object Views,” on page 810 and “User Object Procs,” on page 810 for a listing of
the built-in views and procs.
Data Members
The @hasmember(obname) function, available as a data member for all user objects,
returns a boolean value depending on whether an object called obname currently exists
inside the User Object.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-244-320.jpg)
![The Optimize Command—241
log-likelihood contributions in the objective series LOGL. Note that the first four elements of
the vector BETA correspond to the mean regression coefficients, and the last element is the
parameter for the standard deviation of the error distribution.
Lastly, when designing your subroutine, you should always define the objective to return
NA values for bad control values, since returning an arbitrary value may make numeric
derivatives unreliable at points close to the invalid region.
The Optimize Command
The syntax for the optimize command is:
optimize(options) subroutine_name(arguments)
where subroutine_name is the name of the defined subroutine in your program (or included
programs). The full set of options is provided in optimize (p. 419),
By default, EViews will assume that the first argument of the subroutine is the objective of
the optimization, and that the second argument contains the controls. The default is to max-
imize the objective or sum of the objective values (with the sum taken over the current
workfile sample, if a series).
Specifying the Method and Objective
You may control the type of optimization and which subroutine argument corresponds to
the objective by providing one of the following options to the optimize command:
• max [=integer]
• min [=integer]
• ls [=integer]
• ml [=integer]
The four options correspond to different optimization types: maximization (“max”), minimi-
zation (“min”), least squares (“ls”) and maximum likelihood (“ml”). If the objective is sca-
lar valued only “max” and “min” are allowed.
As the names suggest, “min” and “max” correspond to minimizing and maximizing the
objective. If the objective is multi-valued, optimize will minimize or maximize the sum of
the elements of the objective.
“ls” and “ml” are special forms of minimization and maximization that may be specified
only if the multi-valued objective argument has a value for each observation. “ls” tells
optimize that you wish to perform least squares estimation so the optimizer should mini-
mize the sum-of-squares of the elements of the objective. “ml” informs optimize you wish](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-248-320.jpg)
![288—Chapter 12. Command Reference
seas...................... seasonal adjustment for quarterly and monthly time series (p. 469).
smooth................. exponential smoothing (p. 474).
solve .................... solve a model (p. 478).
stats ..................... descriptive statistics (p. 481).
stepls ................... estimate an equation using stepwise regression (p. 483).
switchreg ............. exogenous and Markov switching regression (p. 486).
testadd ................. likelihood ratio test for adding variables to equation (p. 490).
testdrop................ likelihood ratio test for dropping variables from equation (p. 491).
threshold.............. threshold least squares, including threshold autoregression (p. 491).
tsls....................... estimate an equation using two-stage least squares regression
(p. 496).
ubreak.................. Andrews-Quandt test for unknown breakpoint (p. 500).
uroot.................... unit root test (p. 502).
varest ................... specify and estimate a VAR or VEC (p. 506).
Command Entries
The following section provides an alphabetical listing of commands. Each entry outlines the
command syntax and associated options, and provides examples and cross references.
Register a program file as an EViews Add-in.
Syntax
addin(options) [path]prog_name
registers the specified program file as an EViews Add-in. Note that the program file should
have a “.PRG” extension, which you need not specify in the prog_name.
If you do not provide the optional path specification, EViews looks for the program file in
the default EViews Add-ins directory.
Explicit path specifications containing “.” and “..” (to indicate the current level and one
directory level up) are evaluated relative the directory of the installer program in which the
addin command is specified, or the EViews default directory if addin is run from the com-
mand line.
You may use the special “<addins>”directory keyword in your path specification.
addin Programming Commands](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-295-320.jpg)
![290—Chapter 12. Command Reference
addin(type="graph", menu="Add US Recession Shading",
proc="recshade", docs=".recession shade.txt", desc="Applies US
recession shading to a graph object.") .recshade.prg
registers the file “Recshade.prg” as a graph specific Add-in. The Add-in supports the object-
command recshade, has an object-specific menu item “Add US Recession Shading”, and
has a documentation file “Recession shade.txt”.
addin(type=equation, menu="Simple rolling regression", proc=roll,
docs="<addins>RollRoll.pdf", desc="Rolling Regression -
simple version", url="www.mysite.com/myaddins.xml",
version="1.2") "<addins>Rollroll.prg"
registers the Add-in file “Roll.prg” as an equation specific Add-in. Note that the documenta-
tion and program files are located in the “Roll” subdirectory of the default Add-ins directory.
The XML file located at www.mysite.com/myaddins.xml is used when checking for avail-
able updates for the Add-in, and the current version number is set to “1.2”.
Cross-references
See Chapter 8. “Add-ins,” on page 187 for a detailed discussion of Add-ins.
Register an EViews user object class.
Syntax
adduo(options) [path]definition_name
registers the specified definition file as an EViews user object. Note that the definition file
should have a “.INI” extension.
If you do not provide the optional path specification, EViews looks for the program file in
the default EViews user objects directory.
Explicit path specifications containing “.” and “..” (to indicate the current level and one
directory level up) are evaluated relative the directory of the installer program in which the
adduo command is specified, or the EViews default directory if adduo is run from the com-
mand line.
adduo Programming Commands](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-297-320.jpg)
![arch—291
Options
Examples
adduo(name="roll", desc="Rolling Regression Object") .rolldef.ini
registers the roll class of user object, specifying a description of “Rolling Regression Object”,
and using the definition file rolldef.ini, located in the same folder as the installer program.
adduo(name="resstore", version="1.0",
url="www.mysite.com/myuos.xml") .resstoredef.ini
registers the resstore class of user object, specifying the version number as “1.0”, and using
the XML file located at “www.mysite.com/myuos.xml” to check for updates.
Cross-references
See Chapter 9. “User Objects,” on page 213 for a discussion of user objects.
Estimate generalized autoregressive conditional heteroskedasticity (GARCH) models.
Syntax
arch(p,q,options) y [x1 x2 x3] [@ p1 p2 [@ t1 t2]]
arch(p,q,options) y=expression [@ p1 p2 [@ t1 t2]]
The ARCH command estimates a model with p ARCH terms and q GARCH terms. Note the
order of the arguments in which the ARCH and GARCH terms are entered, which gives prece-
dence to the ARCH term.
name=arg Specify the name of the user object class.
desc=arg Brief description of the user object that will be displayed in
the user object management dialog.
docs=arg Path and filename for the user object documentation.
Determination of the path follows the rules specified above
for the adduo filename.
version=arg Version number of the Add-in. If no version number is sup-
plied, EViews will assume version 1.0.
url=arg Specify the location of an XML file containing information
on the Add-in used for updating the Add-in to the latest
version. If not supplied, EViews will default to an XML file
hosted on the EViews website.
arch Interactive Use Commands](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-298-320.jpg)
![292—Chapter 12. Command Reference
The maximum value for or is 9; values above will be set to 9. The minimum value for
is 1. The minimum value for is 0. If either or is not specified, EViews will assume
a corresponding order of 1. Thus, a GARCH(1, 1) is assumed by default.
After the “ARCH” keyword, specify the dependent variable followed by a list of regressors in
the mean equation.
By default, no exogenous variables (except for the intercept) are included in the conditional
variance equation. If you wish to include variance regressors, list them after the mean equa-
tion using an “@”-sign to separate the mean from the variance equation.
When estimating component ARCH models, you may specify exogenous variance regressors
for the permanent and transitory components. After the mean equation regressors, first list
the regressors for the permanent component, followed by an “@”-sign, then the regressors
for the transitory component. A constant term is always included as a permanent compo-
nent regressor.
Options
General Options
egarch Exponential GARCH.
parch[=arg] Power ARCH. If the optional arg is provided, the power
parameter will be set to that value, otherwise the power
parameter will be estimated.
cgarch Component (permanent and transitory) ARCH.
asy=integer
(default=1)
Number of asymmetric terms in the Power ARCH or
EGARCH model. The maximum number of terms allowed is
9.
thrsh=integer
(default=0)
Number of threshold terms for GARCH and Component
models. The maximum number of terms allowed is 9. For
Component models, “thrsh” must take a value of 0 or 1.
archm=arg ARCH-M (ARCH in mean) specification with the condi-
tional standard deviation (“archm=sd”), the conditional
variance (“archm=var”), or the log of the conditional vari-
ance (“archm= log”) entered as a regressor in the mean
equation.
tdist [=number] Estimate the model assuming that the residuals follow a
conditional Student’s t-distribution (the default is the con-
ditional normal distribution). Providing the optional num-
ber greater than two will fix the degrees of freedom to that
value. If the argument is not provided, the degrees of free-
dom will be estimated.
p q
p q p q](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-299-320.jpg)
![arch—293
ged [=number] Estimate the model assuming that the residuals follow a
conditional GED (the default is the conditional normal dis-
tribution). Providing a positive value for the optional argu-
ment will fix the GED parameter. If the argument is not
provided, the parameter will be estimated.
z Turn of backcasting for both initial MA innovations and ini-
tial variances.
backcast=n Backcast weight to calculate value used as the presample
conditional variance. Weight needs to be greater than 0 and
less than or equal to 1; the default value is 0.7. Note that a
weight of 1 is equivalent to no backcasting, i.e. using the
unconditional residual variance as the presample condi-
tional variance.
optmethod = arg Optimization method: “bfgs” (BFGS); “newton” (Newton-
Raphson), “opg” or “bhhh” (OPG or BHHH), “legacy”
(EViews legacy).
“bfgs” is the default for new equations.
optstep = arg Step method: “marquardt” (Marquardt - default); “dogleg”
(Dogleg); “linesearch” (Line search).
(Applicable when “optmethod=bfgs”, “optmethod=new-
ton” or “optmethod=opg”.)
b Use Berndt-Hall-Hall-Hausman (BHHH) as maximization
algorithm. The default is Marquardt.
(Applicable when “optmethod=legacy”.)
cov=arg Covariance method: “ordinary” (default method based on
inverse of the estimated information matrix), “huber” or
“white” (Huber-White sandwich method), “bollerslev”
(Bollerslev-Wooldridge method).
covinfo = arg Information matrix method: “opg” (OPG); “hessian”
(observed Hessian), “
(Applicable when non-legacy “optmethod=” with
“cov=ordinary”.)
h Bollerslev-Wooldridge robust quasi-maximum likelihood
(QML) covariance/standard errors.
(Applicable for “optmethod=legacy” when estimating
assuming normal errors.)
m=integer Set maximum number of iterations.
c=scalar Set convergence criterion. The criterion is based upon the
maximum of the percentage changes in the scaled coeffi-
cients.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-300-320.jpg)
![294—Chapter 12. Command Reference
GARCH options
Saved results
Most of the results saved for the ls command are also available after ARCH estimation; see
ls (p. 410) for details.
Examples
arch(4, 0, m=1000, cov=bollerslev) sp500 c
estimates an ARCH(4) model with a mean equation consisting of the series SP500 regressed
on a constant. The procedure will perform up to 1000 iterations, and will report Bollerslev-
Wooldridge robust QML standard errors upon completion.
The commands:
c = 0.1
arch(thrsh=1, s, mean=var) @pch(nys) c ar(1)
s Use the current coefficient values in “C” as starting values
(see also param (p. 453)).
s=number Specify a number between zero and one to determine start-
ing values as a fraction of preliminary LS estimates (out of
range values are set to “s=1”).
numericderiv /
-numericderiv
[Do / do not] use numeric derivatives only. If omitted,
EViews will follow the global default.
fastderiv /
-fastderiv
[Do / do not] use fast derivative computation. If omitted,
EViews will follow the global default.
Available only for legacy estimation (“optmeth=legacy”).
showopts /
-showopts
[Do / do not] display the starting coefficient values and
estimation options in the estimation output.
coef=arg Specify the name of the coefficient vector (if specified by
list); the default behavior is to use the “C” coefficient vec-
tor.
prompt Force the dialog to appear from within a program.
p Print estimation results.
vt Variance target of the constant term. (Can’t be used with
integrated specifications).
integrated Restrict GARCH model to be integrated, i.e. IGARCH. (Can’t
be used with variance targeting).](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-301-320.jpg)
![binary—299
auto(4)
The commands:
output(t) c:resultartest.txt
equation eq1.ls cons c y y(-1)
auto(12, p)
perform a regression of CONS on a constant, Y and lagged Y, and test for serial correlation of
up to order twelve. The first line redirects printed tables/text to the “Artest.TXT” file.
Cross-references
See “Serial Correlation LM Test” on page 96 of User’s Guide II for further discussion of the
Breusch-Godfrey test.
See Equation::auto (p. 48) in the Object Reference for the corresponding equation view.
Estimate binary dependent variable models.
Estimates models where the binary dependent variable Y is either zero or one (probit, logit,
gompit).
Syntax
binary(options) y x1 [x2 x3 ...]
binary(options) specification
Options
binary Interactive Use Commands
d=arg
(default=“n”)
Specify likelihood: normal likelihood function, probit
(“n”), logistic likelihood function, logit (“l”), Type I
extreme value likelihood function, Gompit (“x”).
optmethod = arg Optimization method: “bfgs” (BFGS); “newton” (Newton-
Raphson), “opg” or “bhhh” (OPG or BHHH), “legacy”
(EViews legacy).
Newton-Raphson is the default method.
optstep = arg Step method: “marquardt” (Marquardt); “dogleg” (Dog-
leg); “linesearch” (Line search).
Marquardt is the default method.
cov=arg Covariance method: “ordinary” (default method based on
inverse of the estimated information matrix), “huber” or
“white” (Huber-White sandwich method), “glm” (GLM
method).](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-306-320.jpg)
![300—Chapter 12. Command Reference
Examples
To estimate a logit model of Y using a constant, WAGE, EDU, and KIDS, and computing
Huber-White standard errors, you may use the command:
binary(d=l,cov=huber) y c wage edu kids
Note that this estimation uses the default global optimization options. The commands:
param c(1) .1 c(2) .1 c(3) .1
binary(s) y c x2 x3
estimate a probit model of Y on a constant, X2, and X3, using the specified starting values.
The commands:
coef beta_probit = @coefs
matrix cov_probit = @coefcov
covinfo = arg Information matrix method: “opg” (OPG); “hessian”
(observed Hessian - default).
(Applicable when non-legacy “optmethod=”.)
h Huber-White quasi-maximum likelihood (QML) standard
errors and covariances.
(Legacy option applicable when “optmethod=legacy”).
g GLM standard errors and covariances.
(Legacy option applicable when “optmethod=legacy”).
m=integer Set maximum number of iterations.
c=scalar Set convergence criterion. The criterion is based upon the
maximum of the percentage changes in the scaled coeffi-
cients. The criterion will be set to the nearest value
between 1e-24 and 0.2.
s Use the current coefficient values in “C” as starting values
(see also param (p. 453)).
s=number Specify a number between zero and one to determine start-
ing values as a fraction of EViews default values (out of
range values are set to “s=1”).
showopts /
-showopts
[Do / do not] display the starting coefficient values and
estimation options in the estimation output.
coef=arg Specify the name of the coefficient vector (if specified by
list); the default behavior is to use the “C” coefficient vec-
tor.
prompt Force the dialog to appear from within a program.
p Print results.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-307-320.jpg)
![breakls—301
store the estimated coefficients and coefficient covariances in the coefficient vector
BETA_PROBIT and matrix COV_PROBIT.
Cross-references
See “Binary Dependent Variable Models” on page 315 of User’s Guide II for additional dis-
cussion.
See Equation::binary (p. 49) in the Object Reference for the corresponding equation
method.
Estimation by linear least squares regression with breakpoints.
Syntax
breakls(options) y z1 [z2 z3 ...] [@nv x1 x2 x3 ...]
List the dependent variable first, followed by a list of the independent variables that have
coefficients which are allowed to vary across breaks, followed optionally by the keyword
@nv and a list of non-varying coefficient variables.
Options
Breakpoint Options
breakls Interactive Use Commands
method=arg
(default=“seqplus1”)
Breakpoint selection method: “seqplus1” (sequential
tests of single versus breaks), “seqall”
(sequential test of all possible versus breaks),
“glob” (tests of global vs. no breaks), “globplus1”
(tests of versus globally determined breaks),
“globinfo” (information criteria evaluation), “user”
(user-specified break dates).
select=arg Sub-method setting (options depend on “method=”).
(1) if “method=glob”: Sequential (“seq”) (default),
Highest significant (“high”), (“udmax”),
(“wdmax”).
(2) if “method=globinfo”: Schwarz criterion (“bic” or
“sic”) (default), Liu-Wu-Zidek criterion (“lwz”).
trim=arg (default=5) Trimming percentage for determining minimum segment
size (5, 10, 15, 20, 25).
maxbreaks=integer
(default=5)
Maximum number of breakpoints to allow (not applica-
ble if “method=seqall”).
maxlevels=integer
(default=5)
Maximum number of break levels to consider in sequen-
tial testing (applicable when “method=sequall”).
l 1 l
l 1 l
l
l 1 l
UDmax
WDmax](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-308-320.jpg)
![306—Chapter 12. Command Reference
Examples
To change the default directory to “sample data” in the “a:” drive, you may issue the com-
mand:
cd "a:sample data"
Notice that the directory name is surrounded by double quotes. If your name does not con-
tain spaces, you may omit the quotes. The command:
cd a:test
changes the default directory to “a:test”.
cd “<myonedrive>:test
changes the default directory to the cloud location, MYONEDRIVE.
Subsequent save operations will save into the default directory, unless you specify a differ-
ent directory at the time of the operation.
Cross-references
See Chapter 3. “Workfile Basics,” on page 41 of User’s Guide I for further discussion of basic
operations in EViews.
See also “include” on page 713, wfsave (p. 529), pagesave (p. 442), and save (p. 468).
Estimation of censored and truncated models.
Estimates models where the dependent variable is either censored or truncated. The allow-
able specifications include the standard Tobit model.
Syntax
censored(options) y x1 [x2 x3]
censored(options) specification
Options
censored Interactive Use Commands
l=number
(default=0)
Set value for the left censoring limit.
r=number
(default=none)
Set value for the right censoring limit.
l=series_name, i Set series name of the indicator variable for the left censor-
ing limit.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-313-320.jpg)
![censored—307
r=series_name, i Set series name of the indicator variable for the right cen-
soring limit.
t Estimate truncated model.
d=arg
(default=“n”)
Specify error distribution: normal (“n”), logistic (“l”), Type
I extreme value (“x”).
optmethod = arg Optimization method: “bfgs” (BFGS); “newton” (Newton-
Raphson), “opg” or “bhhh” (OPG or BHHH), “legacy”
(EViews legacy).
Newton-Raphson is the default method.
optstep = arg Step method: “marquardt” (Marquardt); “dogleg” (Dog-
leg); “linesearch” (Line search).
Marquardt is the default method.
cov=arg Covariance method: “ordinary” (default method based on
inverse of the estimated information matrix), “huber” or
“white” (Huber-White sandwich method).
covinfo = arg Information matrix method: “opg” (OPG); “hessian”
(observed Hessian - default).
(Applicable when non-legacy “optmethod=”).
h Huber-White quasi-maximum likelihood (QML) standard
errors and covariances.
(Legacy option Applicable when “optmethod=legacy”).
m=integer Set maximum number of iterations.
c=scalar Set convergence criterion. The criterion is based upon the
maximum of the percentage changes in the scaled coeffi-
cients. The criterion will be set to the nearest value
between 1e-24 and 0.2.
s Use the current coefficient values in “C” as starting values
(see also param (p. 453)).
s=number Specify a number between zero and one to determine start-
ing values as a fraction of EViews default values (out of
range values are set to “s=1”).
showopts /
-showopts
[Do / do not] display the starting coefficient values and
estimation options in the estimation output.
coef=arg Specify the name of the coefficient vector (if specified by
list); the default behavior is to use the “C” coefficient vec-
tor.
prompt Force the dialog to appear from within a program.
p Print results.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-314-320.jpg)
![chow—309
Chow test for stability.
Carries out Chow breakpoint or Chow forecast tests for parameter constancy.
Syntax
chow(options) obs1 [obs2 obs3 ...] @ x1 x2 x3
You must provide the breakpoint observations (using dates or observation numbers) to be
tested. To specify more than one breakpoint, separate the breakpoints by a space. For the
Chow breakpoint test, if the equation is specified by list and contains no linear terms, you
may specify a subset of the regressors to be tested for a breakpoint after an “@” sign.
Options
Examples
The commands:
ls m1 c gdp cpi ar(1)
chow 1970Q1 1980Q1
perform a regression of M1 on a constant, GDP, and CPI with first order autoregressive
errors, and employ a Chow breakpoint test to determine whether the parameters before the
1970’s, during the 1970’s, and after the 1970’s are “stable”.
To regress the log of SPOT on a constant, the log of P_US, and the log of P_UK, and to carry
out the Chow forecast test starting from 1973, enter the commands:
ls log(spot) c log(p_us) log(p_uk)
chow(f) 1973
To test whether only the constant term and the coefficient on the log of P_US prior to and
after 1970 are “stable” enter the commands:
chow 1970 @ c log(p_us)
Cross-references
See “Chow's Breakpoint Test” on page 194 of User’s Guide II for further discussion.
chow Interactive Use Commands
f Chow forecast test. For this option, you must specify a sin-
gle breakpoint to test (default performs breakpoint test).
p Print the result of test.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-316-320.jpg)
![312—Chapter 12. Command Reference
Johansen Cointegration Test Syntax
coint(test_option, n, option) ser1 ser2 [...ser3 ser4 ...] [@ x1 x2 x3 ...]
uses the coint keyword followed by the test_option and the number of lags n, and if
desired, an “@”-sign followed by a list of exogenous variables. The first option must be one
of the following six test options:
Options for the Johansen Test
a No deterministic trend in the data, and no intercept or
trend in the cointegrating equation.
b No deterministic trend in the data, and an intercept but no
trend in the cointegrating equation.
c Linear trend in the data, and an intercept but no trend in
the cointegrating equation.
d Linear trend in the data, and both an intercept and a trend
in the cointegrating equation.
e Quadratic trend in the data, and both an intercept and a
trend in the cointegrating equation.
s Summarize the results of all 5 options (a-e).
restrict Impose restrictions as specified by the append (coint)
proc.
m = integer Maximum number of iterations for restricted estimation
(only valid if you choose the restrict option).
c = scalar Convergence criterion for restricted estimation. (only valid
if you choose the restrict option).
save = mat_name Stores test statistics as a named matrix object. The save=
option stores a matrix, where is the num-
ber of endogenous variables in the VAR. The first column
contains the eigenvalues, the second column contains the
maximum eigenvalue statistics, the third column contains
the trace statistics, and the fourth column contains the log
likelihood values. The i-th row of columns 2 and 3 are the
test statistics for rank . The last row is filled with
NAs, except the last column which contains the log likeli-
hood value of the unrestricted (full rank) model.
k 1 4 k
i 1–](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-319-320.jpg)
![coint—313
This type of cointegration testing may be used in a non-panel workfile. For Fisher combined
testing using the Johansen framework, see below. The remaining options for the Johansen
cointegration test are outlined below (“Options for the Johansen Test” on page 312).
Note that the output for cointegration tests displays p-values for the rank test statistics.
These p-values are computed using the response surface coefficients as estimated in MacK-
innon, Haug, and Michelis (1999). The 0.05 critical values are also based on the response
surface coefficients from MacKinnon-Haug-Michelis. Note: the reported critical values
assume no exogenous variables other than an intercept and trend.
Single Equation Test Syntax
coint(method=arg, options) ser1 ser2 [...ser3 ser4 ...] [@determ determ_spec] [@reg-
determ regdeterm_spec]
where
Cointegrating equation specifications that include a constant, linear, or quadratic trends,
should use the “trend=” option to specify those terms. If any of those terms are in the sto-
chastic regressors equations but not in the cointegrating equation, they should be specified
using the “regtrend=” option.
Deterministic trend regressors that are not covered by the list above may be specified using
the keywords @determ and @regdeterm. To specify deterministic trend regressors that enter
into the regressor and cointegrating equations, you should add the keyword @determ fol-
lowed by the list of trend regressors. To specify deterministic trends that enter in the regres-
cvtype=ol Display 0.05 and 0.01 critical values from Osterwald-
Lenum (1992).
This option reproduces the output from version 4. The
default is to display critical values based on the response
surface coefficients from MacKinnon-Haug-Michelis
(1999). Note that the argument on the right side of the
equals sign are letters, not numbers 0-1).
cvsize=arg
(default=0.05)
Specify the size of MacKinnon-Haug-Michelis (1999) criti-
cal values to be displayed. The size must be between
0.0001 and 0.9999; values outside this range will be reset to
the default value of 0.05. This option is ignored if you set
“cvtype=ol”.
prompt Force the dialog to appear from within a program.
p Print results.
method=arg Test method: Engle-Granger residual test (“eg”), Phillips-
Ouliaris residual test (“po”).](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-320-320.jpg)
![316—Chapter 12. Command Reference
HAC Kernel Options:
Panel Syntax
coint(option) ser1 ser2 [...ser3 ser4 ...]
The coint command tests for cointegration among the series in the group. The second form
of the command should be used with panel structured workfiles.
Options for the Panel Tests
For panel cointegration tests, you may specify the type using one of the following keywords:
Depending on the type selected above, the following may be used to indicate deterministic
trends:
lag=arg (default=0) Lag specification: integer (user-specified lag value), “a”
(automatic selection).
infosel=arg
(default=“aic”)
Information criterion for automatic selection: “aic”
(Akaike), “sic” (Schwarz), “hqc” (Hannan-Quinn) (if
“lag=a”).
maxlag=integer Maximum lag-length for automatic selection (optional) (if
“lag=a”). The default is an observation-based maximum.
kern=arg
(default=“bart”)
Kernel shape: “none” (no kernel), “bart” (Bartlett, default),
“bohman” (Bohman), “daniell” (Daniel), “parzen”
(Parzen), “parzriesz” (Parzen-Riesz), “parzgeo” (Parzen-
Geometric), “parzcauchy” (Parzen-Cauchy), “quadspec”
(Quadratic Spectral), “trunc” (Truncated), “thamm”
(Tukey-Hamming), “thann” (Tukey-Hanning), “tparz”
(Tukey-Parzen).
bw=arg
(default=“nwfixed”)
Bandwidth: “fixednw” (Newey-West fixed), “andrews”
(Andrews automatic), “neweywest” (Newey-West auto-
matic), number (User-specified bandwidth).
nwlag=integer Newey-West lag-selection parameter for use in nonpara-
metric bandwidth selection (if “bw=neweywest”).
bwint Use integer portion of bandwidth.
Pedroni (default) Pedroni (1994 and 2004).
Kao Kao (1999)
Fisher Fisher - pooled Johansen](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-323-320.jpg)
![cointreg—319
performs Pedroni’s residual-based panel cointegration test with automatic lag selection with
a maximum lag limit of 3. Automatic selection based on Schwarz criterion.
Cross-references
See Chapter 20. “Cointegration Testing,” on page 961 of User’s Guide II for details on the var-
ious cointegration tests.
See Equation::coint (p. 64) and Group::coint (p. 290) in the Object Reference for the
related object routines.
Estimate a cointegrating equation using Fully Modified OLS (FMOLS), Canonical Cointe-
grating Regression (CCR), or Dynamic OLS (DOLS) in single time series settings, and Panel
FMOLS and DOLS in panel workfiles.
Syntax
cointreg(options) y x1 [x2 x3 ...] [@determ determ_spec] [@regdeterm regdeter-
m_spec]
List the coint keyword, followed by the dependent variable and a list of the cointegrating
variables.
Cointegrating equation specifications that include a constant, linear, or quadratic trends,
should use the “trend=” option to specify those terms. If any of those terms are in the sto-
chastic regressors equations but not in the cointegrating equation, they should be specified
using the “regtrend=” option.
Deterministic trend regressors that are not covered by the list above may be specified using
the keywords @determ and @regdeterm. To specify deterministic trend regressors that enter
into the regressor and cointegrating equations, you should add the keyword @determ fol-
lowed by the list of trend regressors. To specify deterministic trends that enter in the regres-
sor equations but not the cointegrating equation, you should include the keyword
@regdeterm followed by the list of trend regressors.
cointreg Interactive Use Commands](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-326-320.jpg)
![copy—327
See also coint (p. 311).
Add comment to command capture window.
Syntax
commandcap arg
sends arg to the command capture window. Of particular use to add-in writers for sending
notifications to users.
Cross-references
See “Command Capture” on page 817 of the User’s Guide I for further details.
See also statusline (p. 482).
Copy an object, or a set of objects matching a name pattern, within and between workfiles,
workfile pages, and databases. Data in series objects may be frequency converted or match
merged.
Syntax
copy(options) src_spec dest_spec [src_id dest_id]
copy(options) src_spec dest_spec [@src src_ids @dest dest_id]
where src_spec and dest_spec are of the form:
[ctype][container::][page]object_name
There are three parts to the copy command: (1) a specification of the location and names of
the source objects; (2) a specification of the location and names of the destination objects;
(3) optional source and destination IDs if the copy operation involves match merging.
The source and destination objects are specified in multiple (optional) parts: (1) the con-
tainer specification is the name of a workfile or database; (2) the page specification is the
name of a page within a workfile or a subdirectory within a database; and (3) the
object_name specification is the name of an object or a wildcard pattern corresponding to
multiple objects.
The ctype specification is rarely required, but permits you to specify precisely your source or
destination in cases where a database and workfile share the same name. In this case, ctype
may be used to indicate the container to which you are referring by prefixing the container
commandcap Programming Commands
copy Object Container, Data, and File Commands](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-334-320.jpg)
![334—Chapter 12. Command Reference
Note that since databases are read from disk, you may provide a path for the database in the
container specification, as in:
copy "c:my datadba.edb::ser01" ser02
which copies the object SER01 from the database DBA.EDB located in the path “C:MY
DATA” to SER02 in the default workfile page.
copy gd* "c:my datafindat::"
makes a duplicate of all objects in the default page of the current workfile with names start-
ing with “GD” to the database FINDAT in the root of “C:MY DATA”.
Cross-references
See “Copying Objects” on page 315 of User’s Guide I for a discussion of copying and moving
objects.
See also fetch (p. 355), store (p. 484), and Link::linkto (p. 353) in the Object Refer-
ence.
Compute Pearson product-moment (ordinary) correlations for the specified series or
groups.
Syntax
cor(options) arg1 [arg2 arg3...]
where arg1, arg2, etc. are the names of series or groups.
Note that this command is a limited feature version of the group view Group::cor (p. 298)
in the Object Reference.
Options
cor Interactive Use Commands
wgt=name
(optional)
Name of series containing weights.
wgtmethod=arg
(default =
“sstdev”)
Weighting method (when weights are specified using
“weight=”): frequency (“freq”), inverse of variances
(“var”), inverse of standard deviation (“stdev”), scaled
inverse of variances (“svar”), scaled inverse of standard
deviations (“sstdev”).
pairwise Compute using pairwise deletion of observations with
missing cases (pairwise samples).](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-341-320.jpg)
![count—335
Examples
cor height weight age
displays a Pearson correlation matrix for the three series HEIGHT, WEIGHT, and AGE.
Cross-references
See Group::cor (p. 298) in the Object Reference for more general routines for computing
correlations.
See also cov (p. 337). For simple functions to perform the calculations, see @cor (p. 664),
and @cov (p. 665).
Estimates models where the dependent variable is a nonnegative integer count.
Syntax
count(options) y x1 [x2 x3...]
count(options) specification
Follow the count keyword by the name of the dependent variable and a list of regressors.
Options
out=name Basename for saving output. All results will be saved in
Sym matrices named using the string “CORR”, appended to
the basename (e.g., the correlation specified by “out=my”
is saved in the Sym matrix “MYCORR”).
prompt Force the dialog to appear from within a program.
p Print the result.
count Interactive Use Commands
d=arg
(default=“p”)
Likelihood specification: Poisson likelihood (“p”), normal
quasi-likelihood (“n”), exponential likelihood (“e”), nega-
tive binomial likelihood or quasi-likelihood (“b”).
v=positive_num
(default=1)
Specify fixed value for QML parameter in normal and nega-
tive binomial quasi-likelihoods.
optmethod = arg Optimization method: “bfgs” (BFGS); “newton” (Newton-
Raphson), “opg” or “bhhh” (OPG or BHHH), “legacy”
(EViews legacy).
Newton-Raphson is the default method.
3 3](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-342-320.jpg)
![cov—337
count(d=p) y c x1
fit yhat
estimates a Poisson count model of Y on a constant and X1, and saves the fitted values (con-
ditional mean) in the series YHAT.
count(d=p, h) y c x1
estimates the same model with QML standard errors and covariances.
Cross-references
See “Count Models” on page 361 of User’s Guide II for additional discussion.
See Equation::count (p. 79) of the Object Reference for the equivalent equation object
command.
Compute Pearson product-moment (ordinary) covariances for the specified series or
groups.
Syntax
cor arg1 [arg2 arg3...]
where arg1, arg2, etc. are the names of series or groups.
Note that this command is a limited feature version of the group view Group::cov (p. 302)
in the Object Reference.
Options
cov Interactive Use Commands
wgt=name
(optional)
Name of series containing weights.
wgtmethod=arg
(default =
“sstdev”)
Weighting method (when weights are specified using
“weight=”): frequency (“freq”), inverse of variances
(“var”), inverse of standard deviation (“stdev”), scaled
inverse of variances (“svar”), scaled inverse of standard
deviations (“sstdev”).
pairwise Compute using pairwise deletion of observations with
missing cases (pairwise samples).](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-344-320.jpg)
![338—Chapter 12. Command Reference
Examples
cov height weight age
displays a Pearson covariance matrix for the three series HEIGHT, WEIGHT, and AGE.
Cross-references
See Group::cov (p. 302) in the Object Reference for more general routines for computing
covariances.
See also cor (p. 334) .For simple functions to perform the calculations, see @cor (p. 664),
and @cov (p. 665).
Create workfile.
This command has been replaced by wfcreate (p. 510) and pagecreate (p. 434).
Displays cross correlations (correlograms) for a pair of series.
Syntax
cross(n,options) ser1 ser2 [ser3 ...]
You must specify the number of lags n to use in computing the cross correlations as the first
option. EViews will create an untitled group from the specified series and groups, and will
display the cross correlation view for the group.
Options
The following options may be specified inside the parentheses after the number of lags:
out=name Basename for saving output. All results will be saved in
Sym matrices named using the string “CORR”, appended to
the basename (e.g., the correlation specified by “out=my”
is saved in the sym matrix “MYCORR”).
prompt Force the dialog to appear from within a program.
p Print the result.
create Object Container, Data, and File Commands
cross Interactive Use Commands
prompt Force the dialog to appear from within a program.
p Print the cross correlogram.
3 3](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-345-320.jpg)
![data—339
Examples
cross(36) log(m1) dlog(cpi)
displays the cross correlogram between the log of M1 and the first difference of the log of
CPI, using up to 36 leads and lags.
equation eq1.arch sp500 c
eq1.makeresid(s) res_std
cross(24) res_std^2 res_std
The first line estimates a GARCH(1,1) model and the second line retrieves the standardized
residuals. The third line plots the cross correlogram squared standardized residual and the
standardized residual, up to 24 leads and lags. This correlogram provides a rough check of
asymmetry in the ARCH effect.
Cross-references
See “Cross Correlations and Correlograms” on page 559 of User’s Guide I for discussion.
See Group::cross (p. 305) of the Object Reference for the equivalent group view command.
Enter data from keyboard.
Opens an unnamed group window to edit one or more series.
Syntax
data arg1 [arg2 arg3 ...]
Follow the data keyword by a list of series and group names. You can list existing names or
new names. Unrecognized names will cause new series to be added to the workfile. These
series will be initialized with the value “NA”.
Examples
data group1 newx newy
opens a group window containing the series in group GROUP1, and the series NEWX and
NEWY.
Cross-references
See “Entering Data” on page 139 of User’s Guide I for a discussion of the process of entering
data from the keyboard.
data
Object Creation Commands || Object Assignment Commands || Interac-
tive Use Commands](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-346-320.jpg)
![340—Chapter 12. Command Reference
Open or create a database.
If the specified database does not exist, a new (empty) database will be created and opened.
The opened database will become the default database.
Syntax
db(options) [path]db_name [as shorthand_name]
Follow the db command by the name of the database to be opened or to be created (if it
does not already exist). You may include a path name to work with a database not in the
default path.
You may use the “as” keyword to provide an optional shorthand_name or short text label
which may be used to refer to the database in commands and programs. If you leave this
field blank, a default shorthand_name will be assigned automatically.
See “Database Shorthands” on page 311 of User’s Guide I for additional discussion.
Options
See dbopen (p. 344) for a list of available options for working with foreign format data-
bases.
Examples
db findat
opens the database FINDAT in the default path and makes it the default database from
which to store and fetch objects. If the database FINDAT does not already exist, an empty
database named FINDAT will be created and opened.
Cross-references
See Chapter 10. “EViews Databases,” on page 307 of User’s Guide I for a discussion of
EViews databases.
See also dbcreate (p. 342) and dbopen (p. 344).
Make a copy of an existing database.
Syntax
dbcopy [path]source_name [path]copy_name
db Object Container, Data, and File Commands
dbcopy Object Container, Data, and File Commands](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-347-320.jpg)
![342—Chapter 12. Command Reference
Examples
dbcopy usdat c:backupusdat
makes a copy of all files associated with the database USDAT in the default path and stores
it in the “c:backup” directory under the basename “Usdat”.
Cross-references
See Chapter 10. “EViews Databases,” on page 307 of User’s Guide I for a discussion of
EViews databases.
See also dbrename (p. 347) and dbdelete (p. 344).
Create a new database.
Syntax
dbcreate(options) [path]db_name [as shorthand_name]
Follow the dbcreate keyword by a name for the new database. You may include a path
name to create a database not in the default directory. The new database will become the
default database.
You may use the “as” keyword to provide an optional shorthand_name or a short text label
which may be used to refer to the open database in commands and programs. If you leave
this field blank, a default shorthand_name will be assigned automatically. See “Database
Shorthands” on page 311 of the User’s Guide I for additional discussion.
Options
dests=server_id,
destserver
=server_id
Server name
destu=user,
destusername=us
er
Username
destp=pwd,
destpassword
=pwd
Password
dbcreate Object Container, Data, and File Commands
type=arg, t=arg Specify the database type: (see table below). The default is
to create an EViews 7 database.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-349-320.jpg)
![344—Chapter 12. Command Reference
Delete an existing database (all files associated with the specified database).
Syntax
dbdelete [path]db_name
Follow the dbdelete keyword by the name of the database to be deleted. You may include
a path name to delete a database not in the default path.
Examples
dbdelete c:temptestdat
deletes all files associated with the TESTDAT database in the specified directory.
Cross-references
See Chapter 10. “EViews Databases,” on page 307 of User’s Guide I for a discussion of
EViews databases.
See also dbcopy (p. 340) and dbdelete (p. 344).
Open an existing database.
Syntax
dbopen(options) [path]db_name [as shorthand_name]
Follow the dbopen keyword with the name of a database. You should include a path name
to open a database not in the default path. The opened database will become the default
database.
You do not need to specify a database name when opening a Datastream or FRED connection
(“type=datastream” or “type=fred”) as EViews will automatically connect to the proper loca-
tion.
You may use the “as” keyword to provide an optional shorthand_name or a short text label
which is used to refer to the open database in commands and programs. If you leave this
field blank, a default shorthand_name will be assigned automatically.
See “Database Shorthands” on page 311 of User’s Guide I for additional discussion.
By default, EViews will use the extension of the database file to determine type. For exam-
ple, files with the extension “.EDB” will be opened as an EViews database, while files with
dbdelete Object Container, Data, and File Commands
dbopen Object Container, Data, and File Commands](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-351-320.jpg)
![346—Chapter 12. Command Reference
The use of Bloomberg, CEIC, Datastream, DRIPro Link, EcoWin, EIA, FactSet, FRED, IHS
IHS Global Insight, Magellan, and Economy.com databases requires an active connection to
the internet.
In addition, specific types may require installation of additional software. For details see,
“Notes on Particular Formats” on page 337 in User’s Guide I.
The following options may be required when connecting to a remote server:
Examples
dbopen c:dataus1
opens a database named US1 in the C:DATA directory. The command:
dbopen us1
opens a database in the default path. If the specified database does not exist, EViews will
issue an error message. You should use db (p. 340) or dbcreate (p. 342) to create a new
database.
Cross-references
See Chapter 10. “EViews Databases,” on page 307 of User’s Guide I for a discussion of
EViews databases.
See also db (p. 340) and dbcreate (p. 342).
Pack an existing database.
Syntax
dbpack [path]db_name
Follow the dbpack keyword by a database name. You may include a path name to pack a
database not in the default path.
Examples
dbpack findat
s=server_id,
server=server_id
Server name
u=user,
username=user
Username
p=pwd,
password=pwd
Password
dbpack Object Container, Data, and File Commands](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-353-320.jpg)
![dbrename—347
packs the database named FINDAT in the default path.
Cross-references
See “Packing the Database” on page 334 of User’s Guide I for additional discussion.
See also dbrebuild (p. 347).
Rebuild an existing database.
Rebuild a seriously damaged database into a new database file.
Syntax
dbrebuild [path]source_name [path]dest_name
Follow the dbrebuild keyword by the name of the database to be rebuilt, and then a new
database name.
Examples
If you issue the command:
dbrebuild testdat fixed_testdat
EViews will attempt to rebuild the database TESTDAT into the database FIXED_TESTDAT in
the default directory.
Cross-references
See “Maintaining the Database” on page 333 of User’s Guide I for a discussion.
See also dbpack (p. 346).
Rename an existing database.
dbrename renames all files associated with the specified database.
Syntax
dbrename [path]old_name [path]new_name
Follow the dbrename keyword with the current name of an existing database and the new
name for the database.
dbrebuild Object Container, Data, and File Commands
dbrename Object Container, Data, and File Commands](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-354-320.jpg)
![348—Chapter 12. Command Reference
Examples
dbrename testdat mydat
Renames all files associated with the TESTDAT database in the specified directory to MYDAT
in the default directory.
Cross-references
See Chapter 10. “EViews Databases,” on page 307 of User’s Guide I for a discussion of
EViews databases.
See db (p. 340) and dbcreate (p. 342). See also dbcopy (p. 340) and dbdelete (p. 344).
Deletes objects from a workfile or a database.
Syntax
delete(options) arg1 [arg2 arg3 ...]
Follow the keyword by a list of the names of any objects you wish to remove from the cur-
rent workfile. Deleting does not remove objects that have been stored on disk in EViews
database files.
Options
You can delete an object from a database by prefixing the name with the database name and
a double colon. You can use a pattern to delete all objects from a workfile or database with
names that match the pattern. Use the “?” to match any one character and the “*” to match
zero or more characters.
If you use delete in a program file, EViews will delete the listed objects without prompting
you to confirm each deletion.
Examples
To delete all objects in the workfile with names beginning with “TEMP”, you may use the
command:
delete temp*
To delete the objects CONS and INVEST from the database MACRO1, use:
delete macro1::cons macro1::invest
delete Object Utility Commands
noerr Do not error if the object doesn’t exist.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-355-320.jpg)
![350—Chapter 12. Command Reference
Examples
dbcreate dribasic
driconvert dribasic
driconvert c:mydatadridbase
The first line creates a new (empty) database named DRIBASIC in the default directory. The
second line copies all the data in the DRI Basic Economics database into in the DRIBASIC
database. The last example copies the DRI data into the database DRIDBASE that is located
in the C:MYDATA directory.
Cross-references
See Chapter 10. “EViews Databases,” on page 307 of the User’s Guide I for a discussion of
EViews databases.
See also dbcreate (p. 342) and db (p. 340).
Execute a program.
The exec command executes a program. The program may be located in memory or stored
in a program file on disk.
Syntax
exec(options) [path]prog_name(prog_options) [%0 %1 …]
If you wish to pass one or more options to the program, you should enclose them in paren-
theses immediately after the filename. If the program has arguments, you should list them
after the filename.
EViews first checks to see if the specified program is in memory. If the program is not
located, EViews then looks for the program on disk in the EViews Add-ins directory, or in the
specified path. The program file should have a “.PRG” extension, which you need not spec-
ify in the prog_name.
exec Programming Commands](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-357-320.jpg)
![exec—351
Options
Examples
exec rollreg
will run the program “Rollreg.prg” in the EViews add-in directory.
exec(this=graph01) recshade
will run the program “Recshade” in the EViews add-in directory, setting the _this object to
GRAPH01.
exec(4) c:myfilessimul.prg(h=3) xhat
will run the program “Simul.prg” in the path “c:myfiles”, with program option string
“h=3”, the %0 argument set to “XHAT”, and with the maximum error count set to 4.
Note that in contrast to the run command, exec will not stop executing a running program
after returning from the executed program. For example if you have a program containing:
exec simul
print x
integer
(default=1)
Set maximum errors allowed before halting the program in
interactive mode.
Note that the integer option does not apply when using
exec in a program, it only applies when using exec from the
command line. When using exec in a parent program to
execute a child program, the child program inherits the
maximum error count from the parent.
c Run program file without opening a window for display of
the program file.
verbose / quiet Verbose mode in which messages will be sent to the status
line at the bottom of the EViews window (slower execu-
tion), or quiet mode which suppresses workfile display
updates (faster execution).
v / q Same as [verbose / quiet].
ver4 / ver5 Execute program in [version 4 / version 5] compatibility
mode.
this=object_name Set the _this object for the executed program. If omitted,
the executed program will inherit the _this object from
the parent program, or from the current active workfile
object when the exec command is issued from the com-
mand window.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-358-320.jpg)
![352—Chapter 12. Command Reference
the print statement will be executed after running the “Simul.prg” program. If you replace
exec with run, the program will stop after executing the commands in “Simul.prg”.
Cross-references
See “Executing a Program” on page 120 of the User’s Guide I and “The Active Object Key-
word” on page 209 in the Command and Programming Reference for further details.
See also run (p. 467) and include (p. 713).
Exit from EViews (close the EViews application).
You will be prompted to save objects and workfiles which have changed since the last time
they were saved to disk. Be sure to save your workfiles, if desired, since all changes that you
do not save to a disk file will be lost.
Syntax
exit
Cross-references
See also close (p. 310) and save (p. 468).
Expand a workfile.
No longer supported. See the replacement command pagestruct (p. 448).
Factor breakpoint test for stability.
Carries out a factor breakpoint test for parameter constancy.
Syntax
facbreak(options) ser1 [ser2 ser3 ...] @ x1 x2 x3
You must provide one or more series to be used as the factors with which to split the sample
into categories. To specify more than one factor, separate the factors by a space. If the equa-
tion is specified by list and contains no linear terms, you may specify a subset of the regres-
sors to be tested for a breakpoint after an “@” sign.
exit Global Commands
expand Object Container, Data, and File Commands
facbreak Interactive Use Commands](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-359-320.jpg)
![factest—353
Options
Examples
The commands
ls log(spot) c log(p_us) log(p_uk)
facbreak season
perform a regression of the log of SPOT on a constant, the log of P_US, and the log of P_UK,
and employ a factor breakpoint test to determine whether the parameters are stable through
the different values of SEASON.
To test whether only the constant term and the coefficient on the log of P_US are “stable”
enter the commands:
facbreak season @ c log(p_us)
Cross-references
See “Factor Breakpoint Test” on page 179 of User’s Guide II for further discussion.
See also Equation::facbreak (p. 86) and Equation::rls (p. 153) in the Object Refer-
ence.
Specify and estimate a factor analysis model.
Syntax
factest(method=arg, options) x1 [x2 x3...] [@partial z1 z2 z3...]
factest(method=arg, options) matrix_name [[obs] [conditioning]] [@ name1 name2
name3...]
where:
and the available options are specific to the factor estimation method (see “Factor Methods”
on page 179 of the Object Reference).
p Print the result of the test.
factest Interactive Use Commands
method=arg
(default= “ml”)
Factor estimation method: “ml” (maximum likelihood),
“gls” (generalized least squares), “ipf” (iterated principal
factors), “pace” (non-iterative partitioned covariance
matrix estimation), “pf” (principal factors), “uls”
(unweighted least squares)](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-360-320.jpg)
![358—Chapter 12. Command Reference
Use the “notifyillegal” option to display a dialog when fetching the series MYIL-
LEG@LNAME that will suggest a valid name and give you to opportunity to name the object
before it is inserted into a workfile:
fetch(notifyillegal) myilleg@lname
The command:
fetch(p=us_) gdp inv cons
Fetches the series US_GDP, US_INV and US_CONS into workfile series with the same names.
fetch(si=” us equity”) ibm msft
Fetches the database series IBM US EQUITY, and MSFT US EQUITY into workfile series with
names IBM and MSFT.
Cross-references
See Chapter 10. “EViews Databases,” on page 307 of User’s Guide I for a discussion of data-
bases, databank files, and frequency conversion. Appendix A. “Wildcards,” on page 739
describes the use of wildcard characters.
See also store (p. 484), and copy (p. 327).
See Series::setconvert (p. 589) in the Object Reference for information on default con-
version settings.
Computes static forecasts or fitted values from an estimated equation.
When the regressor contains lagged dependent values or ARMA terms, fit uses the actual
values of the dependent variable instead of the lagged fitted values. You may instruct fit to
compare the forecasted data to actual data, and to compute forecast summary statistics.
(Note that we recommend that you instead use the equation proc Equation::fit since it
explicitly specifies the equation of interest.)
Not available for equations estimated using ordered methods; use Equation::makemodel
to create a model using the ordered equation results.
Syntax
fit(options) yhat [y_se]
Following the fit keyword, you should type a name for the forecast series and, optionally, a
name for the series containing the standard errors and, for ARCH specifications, a name for
the conditional variance series.
fit Interactive Use Commands](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-365-320.jpg)
![360—Chapter 12. Command Reference
the fitted index using the cumulative distribution function of the logistic distribution. Note
that YF and YHAT should be identical.
Note that you cannot fit values from an ordered model. You must instead solve the values
from a model. The following lines generate fitted probabilities from an ordered model:
equation eq3.ordered y c x z
eq3.makemodel(oprob1)
solve oprob1
The first line estimates an ordered probit of Y on a constant, X, and Z. The second line
makes a model from the estimated equation with a name OPROB1. The third line solves the
model and computes the fitted probabilities that each observation falls in each category.
Cross-references
To perform dynamic forecasting, use forecast (p. 360).
See Chapter 23. “Forecasting from an Equation,” on page 135 of User’s Guide II for a discus-
sion of forecasting in EViews and Chapter 2. “Discrete and Limited Dependent Variable
Models,” on page 315 of User’s Guide II for forecasting from binary, censored, truncated, and
count models.
See “Forecasting” on page 696 of User’s Guide II for a discussion of forecasting from sspace
models.
See Equation::forecast (p. 90) and Equation::fit (p. 87) in the Object Reference for
the equivalent object commands.
See Equation::makemodel (p. 126) and Model::solve (p. 445) in the Object Reference
for forecasting from systems of equations or ordered equations.
Computes (n-period ahead) dynamic forecasts of the default equation.
forecast computes the forecast using the default equation for all observations in a speci-
fied sample. In some settings, you may instruct forecast to compare the forecasted data to
actual data, and to compute summary statistics.
(Note that we recommend that you instead use the equation proc Equation::forecast
since it explicitly specifies the equation of interest.)
Syntax
forecast(options) yhat [y_se]
forecast Interactive Use Commands](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-367-320.jpg)
![glm—367
In addition to the specification options, there are options for estimation and covariance cal-
culation.
Additional Options
wtype=arg
(default=“istdev”)
Weight specification type: inverse standard deviation (“ist-
dev”), inverse variance (“ivar”), standard deviation
(“stdev”), variance (“var”).
wscale=arg Weight scaling: EViews default (“eviews”), average
(“avg”), none (“none”).
The default setting depends upon the weight type:
“eviews” if “wtype=istdev”, “avg” for all others.
optmethod = arg Optimization method: “bfgs” (BFGS); “newton” (Newton-
Raphson), “opg” or “bhhh” (OPG or BHHH), “fisher” (IRLS
– Fisher Scoring), “legacy” (EViews legacy).
Newton-Raphson is the default method.
optstep = arg Step method: “marquardt” (Marquardt); “dogleg” (Dog-
leg); “linesearch” (Line search).
Marquardt is the default method.
estmeth=arg
(default=”mar-
quardt”)
Legacy estimation algorithm: Quadratic Hill Climbing
(“marquardt”), Newton-Raphson (“newton”), IRLS - Fisher
Scoring (“irls”), BHHH (“bhhh”).
(Applicable when “optmethod=legacy”.)
m=integer Set maximum number of iterations.
c=scalar Set convergence criterion. The criterion is based upon the
maximum of the percentage changes in the scaled coeffi-
cients. The criterion will be set to the nearest value
between 1e-24 and 0.2.
s Use the current coefficient values in estimator coefficient
vector as starting values (see also param (p. 453) in the
Command and Programming Reference).
s=number Specify a number between zero and one to determine start-
ing values as a fraction of EViews default values (out of
range values are set to “s=1”).
showopts / -showopts [Do / do not] display the starting coefficient values and
estimation options in the estimation output.
preiter=arg
(default=0)
Number of IRLS pre-iterations to refine starting values
(only available for non-IRLS estimation).](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-374-320.jpg)
![370—Chapter 12. Command Reference
Estimation by generalized method of moments (GMM).
Syntax
gmm(options) y x1 [x2 x3...] @ z1 [z2 z3...]
gmm(options) specification @ z1 [z2 z3...]
Follow the name of the dependent variable by a list of regressors, followed by the “@” sym-
bol, and a list of instrumental variables which are orthogonal to the residuals. Alternatively,
you can specify an expression using coefficients, an “@” symbol, and a list of instrumental
variables. There must be at least as many instrumental variables as there are coefficients to
be estimated.
In panel settings, you may specify dynamic instruments corresponding to predetermined
variables. To specify a dynamic instrument, you should tag the instrument using “@DYN”,
as in “@DYN(X)”. By default, EViews will use a set of period-specific instruments corre-
sponding to lags from -2 to “-infinity”. You may also specify a restricted lag range using argu-
ments in the “@DYN” tag. For example, to use lags from-5 to “-infinity” you may enter
“@DYN(X, -5)”; to specify lags from -2 to -6, use “@DYN(X, -2, -6)” or “@DYN(X, -6, -2)”.
Note that dynamic instrument specifications may easily generate excessively large numbers
of instruments.
Options
Non-Panel GMM Options
Basic GMM Options
gmm Interactive Use Commands
nocinst Do not include automatically a constant as an instrument.
method=keyword Set the weight updating method. keyword should be one of
the following: “nstep” (N-Step Iterative, or Sequential N-
Step Iterative, default), “converge” (Iterate to Convergence
or Sequential Iterate to Convergence), “simul” (Simultane-
ous Iterate to Convergence), “oneplusone” (One-Step
Weights Plus One Iteration), or “cue” (Continuously Updat-
ing”.
gmmiter=integer Number of weight iterations. Only applicable if the
“method=nstep” option is set.
w=arg Weight series or expression.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-377-320.jpg)
![gmm—371
wtype=arg
(default=“istdev”)
Weight specification type: inverse standard deviation (“ist-
dev”), inverse variance (“ivar”), standard deviation
(“stdev”), variance (“var”).
wscale=arg Weight scaling: EViews default (“eviews”), average
(“avg”), none (“none”).
The default setting depends upon the weight type:
“eviews” if “wtype=istdev”, “avg” for all others.
s Use the current coefficient values in estimator coefficient
vector as starting values for equations specified by list with
AR or MA terms (see also param (p. 453) of the Com-
mand and Programming Reference).
s=number Determine starting values for equations specified by list
with AR or MA terms. Specify a number between zero and
one representing the fraction of TSLS estimates computed
without AR or MA terms to be used. Note that out of range
values are set to “s=1”. Specifying “s=0” initializes coeffi-
cients to zero. By default EViews uses “s=1”.
Does not apply to coefficients for AR and MA terms which
are instead set to EViews determined default values.
m=integer Maximum number of iterations.
c=number Set convergence criterion. The criterion is based upon the
maximum of the percentage changes in the scaled coeffi-
cients. The criterion will be set to the nearest value
between 1e-24 and 0.2.
l=number Set maximum number of iterations on the first-stage itera-
tion to get the one-step weighting matrix.
numericderiv /
-numericderiv
[Do / do not] use numeric derivatives only. If omitted,
EViews will follow the global default.
fastderiv / -fastderiv [Do / do not] use fast derivative computation. If omitted,
EViews will follow the global default.
showopts / -showopts [Do / do not] display the starting coefficient values and
estimation options in the estimation output.
coef=arg Specify the name of the coefficient vector (if specified by
list); the default behavior is to use the “C” coefficient vec-
tor.
prompt Force the dialog to appear from within a program.
p Print results.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-378-320.jpg)
![gmm—375
Note that some options are only available for a subset of specifications.
Examples
In a non-panel workfile, we may estimate equations using the standard GMM options. The
specification:
gmm(instwgt=white,gmmiter=2,nodf) cons c y y(-1) w @ c p(-1) k(-1)
x(-1) tm wg g t
estimates the Klein equation for consumption using GMM with a White diagonal weighting
matrix (two steps and no degree of freedom correction). The command:
gmm(method=cue,instwgt=hac,instlag=1,instkern=thann,
instbw=andrews,nodf) i c y y(-1) k(-1) @ c p(-1) k(-1) x(-1) tm
wg g t
s=number Determine starting values for equations specified by list
with AR terms. Specify a number between zero and one
representing the fraction of TSLS estimates computed with-
out AR terms to be used. Note that out of range values are
set to “s=1”. Specifying “s=0” initializes coefficients to
zero. By default EViews uses “s=1”.
Does not apply to coefficients for AR terms which are
instead set to EViews determined default values.
m=integer Maximum number of iterations.
c=number Set convergence criterion. The criterion is based upon the
maximum of the percentage changes in the scaled coeffi-
cients. The criterion will be set to the nearest value
between 1e-24 and 0.2.
l=number Set maximum number of iterations on the first-stage itera-
tion to get the one-step weighting matrix.
unbalsur Compute SUR factorization in unbalanced data using the
subset of available observations for a cluster.
numericderiv /
-numericderiv
[Do / do not] use numeric derivatives only. If omitted,
EViews will follow the global default.
fastderiv / -fastderiv [Do / do not] use fast derivative computation. If omitted,
EViews will follow the global default.
showopts / -showopts [Do / do not] display the starting coefficient values and
estimation options in the estimation output.
p Print results.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-382-320.jpg)
![heckit—377
The response equation should be the dependent variable followed by a list of regressors. The
selection equation should be a binary dependent variable followed by a list of regressors.
Options
General Options
ML Options
Note these options are not available if the "2step" option, above, is used.
2step Use the Heckman 2-step estimation method. Note that this
option is incompatible with the maximum likelihood
options below.
coef=arg Specify the name of the coefficient vector (if specified by
list); the default behavior is to use the “C” coefficient vec-
tor.
prompt Force the dialog to appear from within a program.
p Print the estimation results.
optmethod =
arg
Optimization method: “bfgs” (BFGS); “newton” (Newton-
Raphson), “opg” or “bhhh” (OPG or BHHH), “legacy”
(EViews legacy).
Newton-Raphson is the default method.
optstep = arg Step method: “marquardt” (Marquardt); “dogleg” (Dog-
leg); “linesearch” (Line search).
Marquardt is the default method.
cov=arg Covariance method: “ordinary” (default method based on
inverse of the estimated information matrix), “huber” or
“white” (Huber-White sandwich methods).,
covinfo = arg Information matrix method: “opg” (OPG); “hessian”
(observed Hessian).
(Applicable when non-legacy “optmethod=”.)
m=integer Set maximum number of iterations.
c=number Set convergence criteria.
numericderiv /
-numericderiv
[Do / do not] use numeric derivatives only. If omitted,
EViews will follow the global default.
showopts /
-showopts
[Do / do not] display the starting coefficient values and
estimation options in the estimation output.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-384-320.jpg)
![hpf—381
date, frequency, and description. This information is displayed in the status line at the bot-
tom of the EViews window. Note that the database_name should refer to the full path to the
Haver database but need not be specified if a default database has been specified in
HAVERDIR.INI.
If several series names are placed on the line, hlabel will gather the information about
each of them, but the information display may scroll by so fast that only the last will be vis-
ible.
Examples
hlabel(c:dataus1) pop
displays the description of the series POP in the US1 database.
Cross-references
See Chapter 10. “EViews Databases,” on page 307 of User’s Guide I for a discussion of
EViews and Haver databases.
See also hfetch (p. 379) and hconvert (p. 378).
Smooth a series using the Hodrick-Prescott filter.
Syntax
hpf(options) series_name filtered_name [@ cycle_name]
Smoothing Options
The degree of smoothing may be specified as an option. You may specify the smoothing as a
value, or using a power rule:
If no smoothing option is specified, EViews will use the power rule with a value of 2.
hpf Interactive Use Commands
lambda=arg Set smoothing parameter value to arg; a larger number
results in greater smoothing.
power=arg
(default=2)
Set smoothing parameter value using the frequency power
rule of Ravn and Uhlig (2002) (the number of periods per
year divided by 4, raised to the power arg, and multiplied
by 1600).
Hodrick and Prescott recommend the value 2; Ravn and
Uhlig recommend the value 4.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-388-320.jpg)
![382—Chapter 12. Command Reference
Other Options
Examples
hpf(lambda=1000) gdp gdp_hp
smooths the GDP series with a smoothing parameter “1000” and saves the smoothed series
as GDP_HP.
Cross-references
See “Hodrick-Prescott Filter” on page 493 of User’s Guide I for details.
See Series::hpf (p. 569) of the Object Reference for the equivalent series object command.
Imports data from a foreign file or a previously saved workfile into the current default
workfile. The import command lets you perform four different types of data importing:
dated reads, match-merge reads, sequential reads, and appends.
Dated imports can only be performed if the destination workfile is a dated workfile. You
must specify the date structure of the source data as part of the import command. EViews
will then match the date structure of the source with that of the destination, and perform
frequency conversion if necessary.
Match-merge imports require both a source ID series and a destination ID series. EViews will
read the source data into the destination workfile based upon matched values of the two ID
series.
Sequential imports will read the source data into the destination workfile by matching the
first observation of the source file to the first observation in the destination workfile's cur-
rent sample, then the second observation of the source with the second observation in the
destination's current sample, and so on.
Appended imports simply append the source data to the end of the destination workfile.
Syntax
The general form of the import command is:
import([type=], options) source_description import_specification [@smpl
smpl_string] [@genr genr_string] [@rename rename_string]
prompt Force the dialog to appear from within a program.
p Print the graph of the smoothed series and the original
series.
import Object Container, Data, and File Commands](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-389-320.jpg)
![384—Chapter 12. Command Reference
• The optional @smpl keyword may be used to specify that data is only imported for
the observations specified by smpl_string. By default, the import will use all of the
observations in the workfile. If @smpl is included, but no smpl_string is included,
then the current default workfile sample is used.
• The optional @genr keyword may be used to generate a new series in the workfile as
part of the import procedure. genr_string may be any valid series creation expression,
and can be an expression based upon one of the imported series. See genr (p. 365)
for information on valid expressions.
• The optional @rename keyword may be used to rename some of the imported series
where rename_string contains pairs of old object names followed by the new names.
See rename (p. 461) for additional discussion.
In the remainder of this discussion, we examine each of the different import types in greater
depth.
Dated Imports
The syntax for a dated import command is:
import([type=], options) source_description @freq frequency start_date [@smpl
smpl_string] [@genr genr_string] [@rename rename_string]
The import_specification consists of the @freq keyword followed by a frequency argument
specifying the frequency of the source data and a start_date to specify the starting date of
the source data. See wfcreate (p. 510) for details on the forms that frequency may take.
Text / ASCII “text”
TSP Portable “t”, “tsp”](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-391-320.jpg)
![386—Chapter 12. Command Reference
When importing data from a higher frequency source into a lower frequency destination:
Examples
import c:tempquarterly.xls @freq q 1990
will import the file QUARTERLY.XLS into the current default workfile. The source file has a
quarterly frequency, starting at 1990.
import(c=s) c:tempquarterly.xls range="GDP_SHEET" @freq q 1990
@rename gdp_per_capita gdp
will import from same file, but instead will use the data on the Excel sheet called
“GDP_SHEET”, and will rename the series GDP_PER_CAPITA to GDP. A frequency conver-
sion method using the sum of the nonmissing observations is used rather than the default
average.
import(mode=p) c:tempannual.txt @freq a 1990 @smpl 1994 1996
will import data from a text file called annual.txt, into the current default workfile. Any data
in the text file that already exists in the destination workfile will be ignored, and for the
remaining series, only the dates between 1994 and 1996 will be imported.
Match-Merge Import
Syntax
import(options) source_description @id id @destid destid [@smpl smpl_string]
[@genr genr_string] [@rename rename_string]
The import_specification consists of the @id keyword and at least one ID series in the
source file, followed by the @destid keyword and at least one ID series in the destination
workfile. The two sets of ID series should be compatible, in that they should contain a sub-
set of identical values that provide information on how observations from the two files
should be matched. If one of the ID series is a date series, you should surround it with the
@date( ) keyword.
c=arg High to low conversion methods removing NAs: “a” (aver-
age of the nonmissing observations), “s” (sum of the non-
missing observations), “f” (first nonmissing observation),
“l” (last nonmissing observation), “x” (maximum nonmiss-
ing observation), “m” (minimum nonmissing observation).
High to low conversion methods propagating NAs: “an” or
“na” (average, propagating missings), “sn” or “ns” (sum,
propagating missings), “fn” or “nf” (first, propagating
missings), “ln” or “nl” (last, propagating missings), “xn”
or “nx” (maximum, propagating missings), “mn” or “nm”
(minimum, propagating missings).](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-393-320.jpg)
![388—Chapter 12. Command Reference
Most of the conversion options should be self-explanatory. As for the others: “first” and
“last” give the first and last non-missing observed for a given group ID; “obs” provides the
number of non-missing values for a given group; “nas” reports the number of NAs in the
group; “unique” will provide the value in the source series if it is the identical for all obser-
vations in the group, and will return NA otherwise; “none” will cause the import to fail if
there are multiple observations in any group—this setting may be used if you wish to pro-
hibit all contractions.
Examples
import(c=max, type=excel) c:datastateunemp.xls @id states
@destid states
will import the file STATEUNEMP.XLS using the ID series STATES in both files as the match
merge criteria. The maximum value is used as a contraction method. Note that although the
“type=excel” option was used, it was not necessary since EViews will automatically detect
the file type based on the file's extension (.xls).
import c:datastategdp.txt colhead=3 delim=comma @id states
@date(year) @destid states @date
will import the file STATEGDP.TXT, specifying that there are three lines of column headers,
and the delimiter for the text file is a comma. The series STATES is used as an ID series in
both files, along with a date series (YEAR for the source file, and the default EViews date
series, @DATE, for the destination workfile). Note that this type of import, with both a
cross-section ID and a date ID is most commonly employed for importing data into panel
workfiles.
import c:datacagdp.xls @id states @date(year) @destid states
@date @genr states="CA"
will import the file CAGDP.XLS into the current workfile. In this particular case the source
file is a time series file for a single state, California. Since the importing is being done into a
panel workfile, the @genr keyword is used to generate a series containing the state identi-
fier, CA, which is then used as the source ID.
Sequential Read
Syntax
import(options) source_description [@smpl smpl_string] [@genr genr_string]
[@rename rename_string]
No import_specification is required for a sequential read.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-395-320.jpg)
![import—389
Options
Examples
import(resize) sales.dta @smpl @all
will import the Stata file SALES.DTA into the current workfile, using the entire workfile
range. If the sales.dta file contains more observations that the current workfile, the current
workfile is resized to accommodate the extra rows of data.
Appended Read
Syntax
import(options) source_description @append [@genr genr_string] [@rename
rename_string]
The import_specification consists of the @append keyword. Note that the @smpl keyword
is not supported for appended import.
resize Extend the destination workfile (if necessary) to include the
entire range of the source data.
link Link the object to the source data so that the values can be
refreshed at a later time.
mode=arg
(default=“o”)
Set the behavior for handling name conflicts when an
imported series already exists in the destination workfile.
arg can be “o” (Completely replace existing series with
source series. Note that values outside of the range of the
source data will be overwritten with NAs), “u” (Overwrite
existing series only for values within the range of the
source data. Destination values outside of the source range
will be unchanged), “ms” (Overwrite existing series, unless
source value is an NA, in which case keep destination val-
ues), “md” (Only overwrite NA values in destination
series), “r” (rename any conflicts), or “p” (do not import
any series which have a name conflict).
page=page_name Optional name for the page into which the data should be
imported.
prompt Force the dialog to appear from within a program.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-396-320.jpg)
![390—Chapter 12. Command Reference
Options
Examples
import(page=demand) demand.txt @append
will append the text file, DEMAND.TXT, to the bottom of the page “demand” in the current
workfile.
Cross-references
See Chapter 3. “Workfile Basics,” on page 41 of User’s Guide I for a discussion of workfiles.
See also wfopen (p. 515), copy (p. 327), pageload (p. 440), read (p. 458), fetch (p. 355),
wfsave (p. 529), and pagesave (p. 442).
Imports observation values stored inside one or more series in a second workfile page into
the attribute fields of objects within the current workfile page.
Syntax
importattr(options) source_page [@keep keeplist @drop droplist]
The source page should contain one series whose values are the names of the objects into
which the attributes should be imported, and one or more additional series containing the
values of each attribute to be imported. By default, the procedure will assume there is an
alpha series called NAME in the source page that contains the object names. The “name=”
option can be used to specify that a different series in the source page contains the object
names.
link Link the object to the source data so that the values can be
refreshed at a later time.
mode=arg
(default=“o”)
Set the behavior for handling name conflicts when an
imported series already exists in the destination workfile.
arg can be “o” (Completely replace existing series with
source series. Note that values outside of the range of the
source data will be overwritten with NAs), “r” (rename any
conflicts), or “p” (do not import any series which have a
name conflict).
page=page_name Optional name for the page into which the data should be
imported.
prompt Force the dialog to appear from within a program.
importattr Object Container, Data, and File Commands](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-397-320.jpg)
![392—Chapter 12. Command Reference
Imports values from series ATTR1 and ATTR2 in the page “Attributes” into attributes “attr1”
and “attr2” of objects in the current workfile. The series OBJNAMES in the page “Attributes”
specifies which objects in the current workfile should have their attribute values updated.
importattr(name=objnames, trim=":") attributes @keep attr1 attr2
removes the “:” from the start and end of the attribute name, if present, since EViews adds
that character in the label view.
Cross-references
See “Workfile Details Display” on page 62 and “Label” on page 405 of User’s Guide I for a
discussion of the workfile details display and, for example, the series object label view.
Imports data from a foreign file into a matrix object in the current workfile.
Syntax
The general form of the importmat command is:
importmat([type=], options) source_description import_specification
• Source_description should contain a description of the file from which the data is to be
imported. The specification of the description is usually just the path and file name of
the file, however you can also specify more precise information. See wfopen (p. 515)
for more details on the specification of source_description.
• The optional “type=” option may be used to specify a source type. For the most part,
you should not need to specify a “type=” option as EViews will automatically deter-
mine the type from the filename. The following table summaries the various source
formats and along with the corresponding “type=” keywords:
importmat Object Container, Data, and File Commands
Option Keywords
Excel (through 2003) “excel”
Excel 2007 (xml) “excelxml”
HTML “html”
Text / ASCII “text”](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-399-320.jpg)
![importmat—393
Options
• import_specification can be used to provide additional information about the file to be
read. The details of import_specification will depend upon the type of file being
imported.
Excel Files
The syntax for reading Excel files is:
importmat(options) source_description [table_description] [variables_description]
The following table_description elements may be used when reading Excel data:
• “range = arg”, where arg is a range of cells to read from the Excel workbook, follow-
ing the standard Excel format [worksheet!][topleft_cell[:bottomright_cell]].
If the worksheet name contains spaces, it should be placed in single quotes. If the
worksheet name is omitted, the cell range is assumed to refer to the currently active
sheet. If only a top left cell is provided, a bottom right cell will be chosen automati-
cally to cover the range of non-empty cells adjacent to the specified top left cell. If
only a sheet name is provided, the first set of non-empty cells in the top left corner of
the chosen worksheet will be selected automatically. As an alternative to specifying
an explicit range, a name which has been defined inside the excel workbook to refer
to a range or cell may be used to specify the cells to read.
• “byrow”, transpose the incoming data. This option allows you to read files where the
series are contained in rows (one row per series) rather than columns.
The optional variables_description may be formed using the elements:
• “colhead=int”, number of table rows to be treated as column headers.
• “types=("arg1","arg2",…)”, user specified data types of the series. If types are pro-
vided they will override the types automatically detected by EViews. You may use any
of the following format keywords: “a” (character data), “f” (numeric data), “d”
(dates), or “w”(EViews automatic detection). This option is rarely required.
• “na="arg1"”, text used to represent observations that are missing from the file. The
text should be enclosed on double quotes.
name=arg Specify a name for the created matrix object.
display=arg Specify a display name for the created matrix object.
page=page_name Optional name for the page into which the matrix should
be created.
mode=o Overwrite an existing object with the same name (only
applicable if the name option is used).](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-400-320.jpg)
![394—Chapter 12. Command Reference
• “scan=[int| all]”, number of rows of the table to scan during automatic format detec-
tion (“scan=all” scans the entire file).
• “firstobs=int”, first observation to be imported from the data (default is 1). This
option may be used to start reading rows from partway through the table.
• “lastobs = int”, last observation to be read from the data (default is last observation
of the file). This option may be used to read only part of the file, which may be useful
for testing.
Excel Examples
importmat "c:data filesdata.xls"
loads the active sheet of DATA.XLSX into a new untitled matrix object.
importmat(name=mymat) "c:data filesdata.xls" range="GDP data"
reads the data contained in the “GDP data” sheet of “Data.XLS” into the MYMAT object.
HTML Files
The syntax for reading HTML pages is:
importmat(options) source_description [table_description] [variables_description]
The following table_description elements may be used when reading an HTML file or page:
• “table = arg”, where arg specifies which HTML table to read in an HTML file/page
containing multiple tables.
When specifying arg, you should remember that tables are named automatically fol-
lowing the pattern “Table01”, “Table02”, “Table03”, etc. If no table name is specified,
the largest table found in the file will be chosen by default. Note that the table num-
bering may include trivial tables that are part of the HTML content of the file, but
would not normally be considered as data tables by a person viewing the page.
• “skip = int”, where int is the number of rows to discard from the top of the HTML
table.
• “byrow”, transpose the incoming data. This option allows you to import files where
the series are contained in rows (one row per series) rather than columns.
The optional variables_description may be formed using the elements:
• “colhead=int”, number of table rows to be treated as column headers.
• “types=("arg1","arg2",…)”, user specified data types of the series. If types are pro-
vided they will override the types automatically detected by EViews. You may use any
of the following format keywords: “a” (character data), “f” (numeric data), “d”
(dates), or “w”(EViews automatic detection). This option is rarely used.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-401-320.jpg)
![importmat—395
• “na="arg1"”, text used to represent observations that are missing from the file. The
text should be enclosed on double quotes.
• “scan=[int|all]”, number of rows of the table to scan during automatic format detec-
tion (“scan=all” scans the entire file).
• “firstobs=int”, first observation to be imported from the table of data (default is 1).
This option may be used to start reading rows from partway through the table.
• “lastobs = int”, last observation to be read from the table of data (default is last
observation of the file). This option may be used to read only part of the file, which
may be useful for testing.
HTML Examples
importmat "c:data.html"
loads into a new untitled matrix object the data located in the HTML file “Data.HTML”
located on the C: drive
importmat(type=html, name=forexmat)
"http://www.tradingroom.com.au/apps/mkt/forex.ac" colhead=3
loads into a matrix object called FOREXMAT the data with the given URL located on the
website site “http://www.tradingroom.com.au”. The column header is set to three rows.
Text and Binary Files
The syntax for reading text or binary files is:
importmat(options) source_description [table_description] [variables_description]
If a table_description is not provided, EViews will attempt to read the file as a free-format
text file. The following table_description elements may be used when reading a text or
binary file:
• “ftype = [ascii|binary]” specifies whether numbers and dates in the file are stored in
a human readable text (ASCII), or machine readable (Binary) form.
• “rectype = [crlf|fixed|streamed]” describes the record structure of the file:
“crlf”, each row in the output table is formed using a fixed number of lines from
the file (where lines are separated by carriage return/line feed sequences). This is
the default setting.
“fixed”, each row in the output table is formed using a fixed number of charac-
ters from the file (specified in “reclen= arg”). This setting is typically used for
files that contain no line breaks.
“streamed”, each row in the output table is formed by reading a fixed number of
fields, skipping across lines if necessary. This option is typically used for files that
contain line breaks, but where the line breaks are not relevant to how rows from
the data should be formed.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-402-320.jpg)
![396—Chapter 12. Command Reference
• “reclines =int”, number of lines to use in forming each row when “rectype=crlf”
(default is 1).
• “reclen=int”, number of bytes to use in forming each row when “rectype=fixed”.
• “recfields=int”, number of fields to use in forming each row when “rec-
type=streamed”.
• “skip=int”, number of lines (if rectype is “crlf”) or bytes (if rectype is not “crlf”) to
discard from the top of the file.
• “comment=string“, where string is a double-quoted string, specifies one or more
characters to treat as a comment indicator. When a comment indicator is found,
everything on the line to the right of where the comment indicator starts is ignored.
• “emptylines=[keep|drop]”, specifies whether empty lines should be ignored
(“drop”), or treated as valid lines (“keep”) containing missing values. The default is
to ignore empty lines.
• “tabwidth=int”, specifies the number of characters between tab stops when tabs are
being replaced by spaces (default=8). Note that tabs are automatically replaced by
spaces whenever they are not being treated as a field delimiter.
• “fieldtype=[delim|fixed|streamed|undivided]”, specifies the structure of fields within
a record:
“Delim”, fields are separated by one or more delimiter characters
“Fixed”, each field is a fixed number of characters
“Streamed”, fields are read from left to right, with each field starting immediately
after the previous field ends.
“Undivided”, read entire record as a single series.
• “quotes=[single|double|both|none]”, specifies the character used for quoting fields,
where “single” is the apostrophe, “double” is the double quote character, and “both”
means that either single or double quotes are allowed (default is “both”). Characters
contained within quotes are never treated as delimiters.
• “singlequote“, same as “quotes = single”.
• “delim=[comma|tab|space|dblspace|white|dblwhite]”, specifies the character(s) to
treat as a delimiter. “White” means that either a tab or a space is a valid delimiter. You
may also use the abbreviation “d=” in place of “delim=”.
• “custom="arg1"”, specifies custom delimiter characters in the double quoted string.
Use the character “t” for tab, “s” for space and “a” for any character.
• “mult=[on|off]”, to treat multiple delimiters as one. Default value is “on” if “delim”
is “space”, “dblspace”, “white”, or “dblwhite”, and “off” otherwise.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-403-320.jpg)
![importmat—397
• “endian = [big|little]”, selects the endianness of numeric fields contained in binary
files.
• “string = [nullterm|nullpad|spacepad]”, specifies how strings are stored in binary
files. If “nullterm”, strings shorter than the field width are terminated with a single
zero character. If “nullpad”, strings shorter than the field width are followed by extra
zero characters up to the field width. If “spacepad”, strings shorter than the field
width are followed by extra space characters up to the field width.
• “byrow”, transpose the incoming data. This option allows you to import files where
the series are contained in rows (one row per series) rather than columns.
A central component of the table_description element is the format statement. You may
specify the data format using the following table descriptors:
• Fortran Format:
fformat=([n1]Type[Width][.Precision], [n2]Type[Width][.Precision], ...)
where Type specifies the underlying data type, and may be one of the following,
I - integer
F - fixed precision
E - scientific
A - alphanumeric
X - skip
and n1, n2, ... are the number of times to read using the descriptor (default=1). More
complicated Fortran compatible variations on this format are possible.
• Column Range Format:
rformat="[n1]Type[Width][.Precision], [n2]Type[Width][.Precision], ...)"
where optional type is “$” for string or “#” for number, and n1, n2, n3, n4, etc. are the
range of columns containing the data.
• C printf/scanf Format:
cformat="fmt"
where fmt follows standard C language (printf/scanf) format rules.
The optional variables_description may be formed using the elements:
• “colhead=int”, number of table rows to be treated as column headers.
• “types=("arg1","arg2",…)”, user specified data types of the series. If types are pro-
vided they will override the types automatically detected by EViews. You may use any
of the following format keywords: “a” (character data), “f” (numeric data), “d”
(dates), or “w”(EViews automatic detection). This option is rarely used.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-404-320.jpg)
![398—Chapter 12. Command Reference
• “na="arg1"”, text used to represent observations that are missing from the file. The
text should be enclosed on double quotes.
• “scan=[int|all]”, number of rows of the table to scan during automatic format detec-
tion (“scan=all” scans the entire file).
• “firstobs=int”, first observation to be imported from the table of data (default is 1).
This option may be used to start reading rows from partway through the table.
• “lastobs = int”, last observation to be read from the table of data (default is last
observation of the file). This option may be used to read only part of the file, which
may be useful for testing.
Text and Binary File Examples (.txt, .csv, etc.)
importmat c:data.csv skip=5
reads “Data.CSV” into a new unnamed matrix object, skipping the first 5 rows.
importtbl(type=text, name=matrix01) c:date.txt delim=comma
loads the comma delimited data DATE.TXT into the MATRIX01 matrix object.
Cross-references
See Chapter 3. “Workfile Basics,” on page 41 of User’s Guide I for a discussion of workfiles.
See also wfopen (p. 515), copy (p. 327), pageload (p. 440), read (p. 458), fetch (p. 355),
wfsave (p. 529), and pagesave (p. 442).
Imports data from a foreign file into a table object in the current workfile.
Syntax
The general form of the importtbl command is:
importtbl([type=], options) source_description import_specification
• Source_description should contain a description of the file from which the data is to be
imported. The specification of the description is usually just the path and file name of
the file, however you can also specify more precise information. See wfopen (p. 515)
for more details on the specification of source_description.
• The optional “type=” option may be used to specify a source type. For the most part,
you should not need to specify a “type=” option as EViews will automatically deter-
mine the type from the filename. The following table summaries the various source
formats and along with the corresponding “type=” keywords:
importtbl Object Container, Data, and File Commands](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-405-320.jpg)
![importtbl—399
Options
• import_specification can be used to provide additional information about the file to be
read. The details of import_specification will depend upon the type of file being
imported.
Excel Files
The syntax for reading Excel files is:
importtbl(options) source_description [table_description] [variables_description]
The following table_description elements may be used when reading Excel data:
• “range = arg”, where arg is a range of cells to read from the Excel workbook, follow-
ing the standard Excel format [worksheet!][topleft_cell[:bottomright_cell]].
If the worksheet name contains spaces, it should be placed in single quotes. If the
worksheet name is omitted, the cell range is assumed to refer to the currently active
sheet. If only a top left cell is provided, a bottom right cell will be chosen automati-
cally to cover the range of non-empty cells adjacent to the specified top left cell. If
only a sheet name is provided, the first set of non-empty cells in the top left corner of
the chosen worksheet will be selected automatically. As an alternative to specifying
an explicit range, a name which has been defined inside the excel workbook to refer
to a range or cell may be used to specify the cells to read.
• “byrow”, transpose the incoming data. This option allows you to read files where the
series are contained in rows (one row per series) rather than columns.
The optional variables_description may be formed using the elements:
• “colhead=int”, number of table rows to be treated as column headers.
Option Keywords
Excel (through 2003) “excel”
Excel 2007 (xml) “excelxml”
HTML “html”
Text / ASCII “text”
name=arg Specify a name for the created table object.
display=arg Specify a display name for the created table object.
page=page_name Optional name for the page into which the table should be
created.
mode=o Overwrite an existing object with the same name (only
applicable if the name option is used).](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-406-320.jpg)
![400—Chapter 12. Command Reference
• “types=("arg1","arg2",…)”, user specified data types of the series. If types are pro-
vided they will override the types automatically detected by EViews. You may use any
of the following format keywords: “a” (character data), “f” (numeric data), “d”
(dates), or “w”(EViews automatic detection). This option is rarely required.
• “na="arg1"”, text used to represent observations that are missing from the file. The
text should be enclosed on double quotes.
• “scan=[int| all]”, number of rows of the table to scan during automatic format detec-
tion (“scan=all” scans the entire file).
• “firstobs=int”, first observation to be imported from the data (default is 1). This
option may be used to start reading rows from partway through the table.
• “lastobs = int”, last observation to be read from the data (default is last observation
of the file). This option may be used to read only part of the file, which may be useful
for testing.
Excel Examples
importtbl "c:data filesdata.xls"
loads the active sheet of DATA.XLSX into a new untitled table object.
importtbl(name=mytbl) "c:data filesdata.xls" range="GDP data"
reads the data contained in the “GDP data” sheet of “Data.XLS” into the MYTBL object.
HTML Files
The syntax for reading HTML pages is:
importtbl(options) source_description [table_description] [variables_description]
The following table_description elements may be used when reading an HTML file or page:
• “table = arg”, where arg specifies which HTML table to read in an HTML file/page
containing multiple tables.
When specifying arg, you should remember that tables are named automatically fol-
lowing the pattern “Table01”, “Table02”, “Table03”, etc. If no table name is specified,
the largest table found in the file will be chosen by default. Note that the table num-
bering may include trivial tables that are part of the HTML content of the file, but
would not normally be considered as data tables by a person viewing the page.
• “skip = int”, where int is the number of rows to discard from the top of the HTML
table.
• “byrow”, transpose the incoming data. This option allows you to import files where
the series are contained in rows (one row per series) rather than columns.
The optional variables_description may be formed using the elements:](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-407-320.jpg)
![importtbl—401
• “colhead=int”, number of table rows to be treated as column headers.
• “types=("arg1","arg2",…)”, user specified data types of the series. If types are pro-
vided they will override the types automatically detected by EViews. You may use any
of the following format keywords: “a” (character data), “f” (numeric data), “d”
(dates), or “w”(EViews automatic detection). This option is rarely used.
• “na="arg1"”, text used to represent observations that are missing from the file. The
text should be enclosed on double quotes.
• “scan=[int|all]”, number of rows of the table to scan during automatic format detec-
tion (“scan=all” scans the entire file).
• “firstobs=int”, first observation to be imported from the table of data (default is 1).
This option may be used to start reading rows from partway through the table.
• “lastobs = int”, last observation to be read from the table of data (default is last
observation of the file). This option may be used to read only part of the file, which
may be useful for testing.
HTML Examples
importtbl "c:data.html"
loads into a new untitled table object the data located in the HTML file “Data.HTML”
located on the C: drive
importtbl(type=html, name=forextbl)
"http://www.tradingroom.com.au/apps/mkt/forex.ac" colhead=3
loads into a table object called FOREXTBL the data with the given URL located on the web-
site site “http://www.tradingroom.com.au”. The column header is set to three rows.
Text and Binary Files
The syntax for reading text or binary files is:
importtbl(options) source_description [table_description] [variables_description]
If a table_description is not provided, EViews will attempt to read the file as a free-format
text file. The following table_description elements may be used when reading a text or
binary file:
• “ftype = [ascii|binary]” specifies whether numbers and dates in the file are stored in
a human readable text (ASCII), or machine readable (Binary) form.
• “rectype = [crlf|fixed|streamed]” describes the record structure of the file:
“crlf”, each row in the output table is formed using a fixed number of lines from
the file (where lines are separated by carriage return/line feed sequences). This is
the default setting.
“fixed”, each row in the output table is formed using a fixed number of charac-](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-408-320.jpg)
![402—Chapter 12. Command Reference
ters from the file (specified in “reclen= arg”). This setting is typically used for
files that contain no line breaks.
“streamed”, each row in the output table is formed by reading a fixed number of
fields, skipping across lines if necessary. This option is typically used for files that
contain line breaks, but where the line breaks are not relevant to how rows from
the data should be formed.
• “reclines =int”, number of lines to use in forming each row when “rectype=crlf”
(default is 1).
• “reclen=int”, number of bytes to use in forming each row when “rectype=fixed”.
• “recfields=int”, number of fields to use in forming each row when “rec-
type=streamed”.
• “skip=int”, number of lines (if rectype is “crlf”) or bytes (if rectype is not “crlf”) to
discard from the top of the file.
• “comment=string“, where string is a double-quoted string, specifies one or more
characters to treat as a comment indicator. When a comment indicator is found,
everything on the line to the right of where the comment indicator starts is ignored.
• “emptylines=[keep|drop]”, specifies whether empty lines should be ignored
(“drop”), or treated as valid lines (“keep”) containing missing values. The default is
to ignore empty lines.
• “tabwidth=int”, specifies the number of characters between tab stops when tabs are
being replaced by spaces (default=8). Note that tabs are automatically replaced by
spaces whenever they are not being treated as a field delimiter.
• “fieldtype=[delim|fixed|streamed|undivided]”, specifies the structure of fields within
a record:
“Delim”, fields are separated by one or more delimiter characters
“Fixed”, each field is a fixed number of characters
“Streamed”, fields are read from left to right, with each field starting immediately
after the previous field ends.
“Undivided”, read entire record as a single series.
• “quotes=[single|double|both|none]”, specifies the character used for quoting fields,
where “single” is the apostrophe, “double” is the double quote character, and “both”
means that either single or double quotes are allowed (default is “both”). Characters
contained within quotes are never treated as delimiters.
• “singlequote“, same as “quotes = single”.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-409-320.jpg)
![importtbl—403
• “delim=[comma|tab|space|dblspace|white|dblwhite]”, specifies the character(s) to
treat as a delimiter. “White” means that either a tab or a space is a valid delimiter. You
may also use the abbreviation “d=” in place of “delim=”.
• “custom="arg1"”, specifies custom delimiter characters in the double quoted string.
Use the character “t” for tab, “s” for space and “a” for any character.
• “mult=[on|off]”, to treat multiple delimiters as one. Default value is “on” if “delim”
is “space”, “dblspace”, “white”, or “dblwhite”, and “off” otherwise.
• “endian = [big|little]”, selects the endianness of numeric fields contained in binary
files.
• “string = [nullterm|nullpad|spacepad]”, specifies how strings are stored in binary
files. If “nullterm”, strings shorter than the field width are terminated with a single
zero character. If “nullpad”, strings shorter than the field width are followed by extra
zero characters up to the field width. If “spacepad”, strings shorter than the field
width are followed by extra space characters up to the field width.
• “byrow”, transpose the incoming data. This option allows you to import files where
the series are contained in rows (one row per series) rather than columns.
A central component of the table_description element is the format statement. You may
specify the data format using the following table descriptors:
• Fortran Format:
fformat=([n1]Type[Width][.Precision], [n2]Type[Width][.Precision], ...)
where Type specifies the underlying data type, and may be one of the following,
I - integer
F - fixed precision
E - scientific
A - alphanumeric
X - skip
and n1, n2, ... are the number of times to read using the descriptor (default=1). More
complicated Fortran compatible variations on this format are possible.
• Column Range Format:
rformat="[n1]Type[Width][.Precision], [n2]Type[Width][.Precision], ...)"
where optional type is “$” for string or “#” for number, and n1, n2, n3, n4, etc. are the
range of columns containing the data.
• C printf/scanf Format:
cformat="fmt"
where fmt follows standard C language (printf/scanf) format rules.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-410-320.jpg)
![404—Chapter 12. Command Reference
The optional variables_description may be formed using the elements:
• “colhead=int”, number of table rows to be treated as column headers.
• “types=("arg1","arg2",…)”, user specified data types of the series. If types are pro-
vided they will override the types automatically detected by EViews. You may use any
of the following format keywords: “a” (character data), “f” (numeric data), “d”
(dates), or “w”(EViews automatic detection). This option is rarely used.
• “na="arg1"”, text used to represent observations that are missing from the file. The
text should be enclosed on double quotes.
• “scan=[int|all]”, number of rows of the table to scan during automatic format detec-
tion (“scan=all” scans the entire file).
• “firstobs=int”, first observation to be imported from the table of data (default is 1).
This option may be used to start reading rows from partway through the table.
• “lastobs = int”, last observation to be read from the table of data (default is last
observation of the file). This option may be used to read only part of the file, which
may be useful for testing.
Text and Binary File Examples (.txt, .csv, etc.)
importtbl c:data.csv skip=5
reads “Data.CSV” into a new unnamed table object, skipping the first 5 rows.
importtbl(type=text, name=table01) c:date.txt delim=comma
loads the comma delimited data DATE.TXT into the TABLE01 table object.
Cross-references
See Chapter 3. “Workfile Basics,” on page 41 of User’s Guide I for a discussion of workfiles.
See also wfopen (p. 515), copy (p. 327), pageload (p. 440), read (p. 458), fetch (p. 355),
wfsave (p. 529), and pagesave (p. 442).
Limited Information Maximum Likelihood and K-class Estimation.
Syntax
liml(options) y c x1 [x2 x3 ...] @ z1 [z2 z3 ...]
liml(options) specification @ z1 [z2 z3 ...]
To use the liml command, list the dependent variable first, followed by the regressors, then
any AR or MA error specifications, then an “@”-sign, and finally, a list of exogenous instru-
ments.
liml Interactive Use Commands](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-411-320.jpg)
![liml—405
You may estimate nonlinear equations or equations specified with formulas by first provid-
ing a specification, then listing the instrumental variables after an “@”-sign. There must be
at least as many instrumental variables as there are independent variables. All exogenous
variables included in the regressor list should also be included in the instrument list. A con-
stant is included in the list of instrumental variables, unless the noconst option is specified.
Options
noconst Do not include a constant in the instrumental list. Without
this option, a constant will always be included as an instru-
ment, even if not specified explicitly.
w=arg Weight series or expression.
wtype=arg
(default=“istdev”)
Weight specification type: inverse standard deviation
(“istdev”), inverse variance (“ivar”), standard deviation
(“stdev”), variance (“var”).
wscale=arg Weight scaling: EViews default (“eviews”), average
(“avg”), none (“none”).
The default setting depends upon the weight type:
“eviews” if “wtype=istdev”, “avg” for all others.
kclass=number Set the value of in the K-class estimator. If omitted, LIML
is performed, and is calculated as part of the estimation
procedure.
se = arg
(default=“iv”)
Set the standard-error calculation type: IV based
(“se=iv”), K-Class based (“se=kclass”), Bekker
(“se=bekk”), or Hansen, Hausman, and Newey
(“se=hhn”).
m=integer Set maximum number of iterations.
c=number Set convergence criterion. The criterion is based upon the
maximum of the percentage changes in the scaled coeffi-
cients. The criterion will be set to the nearest value
between 1e-24 and 0.2.
numericderiv /
-numericderiv
[Do / do not] use numeric derivatives only. If omitted,
EViews will follow the global default.
fastderiv /
-fastderiv
[Do / do not] use fast derivative computation. If omitted,
EViews will follow the global default.
Available only for legacy estimation (“optmeth=legacy”).
showopts /
-showopts
[Do / do not] display the starting coefficient values and
estimation options in the estimation output.
k
k](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-412-320.jpg)
![logmode—407
Estimate binary models with logistic errors.
Provide for backward compatibility. Equivalent to issuing the command, binary with the
option “(d=l)”.
See binary (p. 299).
Set the log settings of specified message types.
Activates or deactivates the logging of specified message types if message types are set to be
program controlled.
Syntax
logmode msgtype_list
where msgtype_list is a list of the message types to update.
Options
Message type options
logit Interactive Use Commands
logmode Programming Commands
all/-all [Show/Do not show] all messages.
error/-error, e/-e [Show/Do not show] error messages.
logmsg /-logmsg,
l/ -l
[Show/Do not show] logmsg messages.
program/-program,
p/-p
[Show/Do not show] program lines.
statusline/-sta-
tusline, s / -s
[Show/Do not show] status line messages.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-414-320.jpg)
![408—Chapter 12. Command Reference
Note that using logmode with debug will override all subsequent (in either the current pro-
gram or any program run using exec or run), logmode with hideprog and -error specifi-
cations. In particular, debug will override subsequent logmode addin statements.
Examples
logmode p
turns on logging of program lines of code. Note that by default all message types are initially
turned off.
logmode error -p
Building on the first command, this activates the logging of errors and deactivates the log-
ging of program lines.
logmode -all s
turns off logging of all types, with exception to status line messages. Note the order of mes-
sage types is important. For example,
logmode p -all
will initially activate the logging of program lines, but following p with -all will deactivate
program lines as well as any other messages that have been previously activated.
Cross-references
See “Program Message Logging” on page 135 for details. See also “Program Modes” on
page 134.
See also logclear (p. 406), logmsg (p. 409), and logsave (p. 409).
hideprogline/-
hideprogline
[Hide/Do not hide] the program line when reporting errors
encountered during execution.
addin/-addin [Show/Do not show] messages generally appropriate for
addin error reporting. addin is equivalent to the command
and program mode (“Program Modes” on page 134) state-
ments:
logmode hideprogline -error
mode quiet
debug Show messages generally appropriate for debugging of pro-
grams. Equivalent to the command:
logmode -hideprogline error](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-415-320.jpg)
![410—Chapter 12. Command Reference
Estimation by linear or nonlinear least squares regression.
When the current workfile has a panel structure, ls also estimates cross-section weighed
least squares, feasible GLS, and fixed and random effects models.
Syntax
ls(options) y x1 [x2 x3 ...]
ls(options) specification
For linear specifications, list the dependent variable first, followed by a list of the indepen-
dent variables. Use a “C” if you wish to include a constant or intercept term; unlike some
programs, EViews does not automatically include a constant in the regression. You may add
AR, MA, SAR, and SMA error specifications, a D fractional differencing term, and PDL spec-
ifications for polynomial distributed lags. If you include lagged variables, EViews will adjust
the sample automatically, if necessary.
Both dependent and independent variables may be created from existing series using stan-
dard EViews functions and transformations. EViews treats the equation as linear in each of
the variables and assigns coefficients C(1), C(2), and so forth to each variable in the list.
Linear or nonlinear single equations may also be specified by explicit equation. You should
specify the equation as a formula. The parameters to be estimated should be included
explicitly: “C(1)”, “C(2)”, and so forth (assuming that you wish to use the default coefficient
vector “C”). You may also declare an alternative coefficient vector using coef and use these
coefficients in your expressions.
Options
Non-Panel LS Options
ls Interactive Use Commands
w=arg Weight series or expression.
Note: we recommend that, absent a good reason, you
employ the default settings Inverse std. dev. weights
(“wtype=istdev”) with EViews default scaling
(“wscale=eviews”) for backward compatibility with ver-
sions prior to EViews 7.
wtype=arg
(default=“istdev”)
Weight specification type: inverse standard deviation (“ist-
dev”), inverse variance (“ivar”), standard deviation
(“stdev”), variance (“var”).](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-417-320.jpg)
![ls—413
Note: not all options are available for all equation methods. See the User’s Guide II for details
on each estimation method.
Panel LS Options
numericderiv /
-numericderiv
[Do / do not] use numeric derivatives only. If omitted,
EViews will follow the global default.
fastderiv / -fastderiv [Do / do not] use fast derivative computation. If omitted,
EViews will follow the global default.
Available only for legacy estimation (“optmeth=legacy”).
showopts /
-showopts
[Do / do not] display the starting coefficient values and
estimation options in the estimation output.
coef=arg Specify the name of the coefficient vector (if specified by
list); the default behavior is to use the “C” coefficient vec-
tor.
prompt Force the dialog to appear from within a program.
p Print estimation results.
cx=arg Cross-section effects: (default) none, fixed effects
(“cx=f”), random effects (“cx=r”).
per=arg Period effects: (default) none, fixed effects (“per=f”), ran-
dom effects (“per=r”).
wgt=arg GLS weighting: (default) none, cross-section system
weights (“wgt=cxsur”), period system weights
(“wgt=persur”), cross-section diagonal weighs
(“wgt=cxdiag”), period diagonal weights (“wgt=per-
diag”).
cov=arg Coefficient covariance method: (default) ordinary, White
cross-section system robust (“cov=cxwhite”), White
period system robust (“cov=perwhite”), White heteroske-
dasticity robust (“cov=stackedwhite”), Cross-section sys-
tem robust/PCSE (“cov=cxsur”), Period system
robust/PCSE (“cov=persur”), Cross-section heteroskedas-
ticity robust/PCSE (“cov=cxdiag”), Period heteroskedastic-
ity robust/PCSE (“cov=perdiag”).
keepwgts Keep full set of GLS weights used in estimation with object,
if applicable (by default, only small memory weights are
saved).
rancalc=arg
(default=“sa”)
Random component method: Swamy-Arora (“ran-
calc=sa”), Wansbeek-Kapteyn (“rancalc=wk”), Wallace-
Hussain (“rancalc=wh”).](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-420-320.jpg)
![414—Chapter 12. Command Reference
nodf Do not perform degree of freedom corrections in computing
coefficient covariance matrix. The default is to use degree
of freedom corrections.
coef=arg Specify the name of the coefficient vector (if specified by
list); the default behavior is to use the “C” coefficient vec-
tor.
iter=arg (default=
“onec”)
Iteration control for GLS specifications: perform one weight
iteration, then iterate coefficients to convergence
(“iter=onec”), iterate weights and coefficients simultane-
ously to convergence (“iter=sim”), iterate weights and
coefficients sequentially to convergence (“iter=seq”), per-
form one weight iteration, then one coefficient step
(“iter=oneb”).
Note that random effects models currently do not permit
weight iteration to convergence.
unbalsur Compute SUR factorization in unbalanced data using the
subset of available observations for a cluster.
m=integer Set maximum number of iterations.
c=scalar Set convergence criterion. The criterion is based upon the
maximum of the percentage changes in the scaled coeffi-
cients. The criterion will be set to the nearest value
between 1e-24 and 0.2.
s Use the current coefficient values in estimator coefficient
vector as starting values for equations specified by list with
AR terms (see also param (p. 453) of the Command and
Programming Reference).
s=number Determine starting values for equations specified by list
with AR terms. Specify a number between zero and one
representing the fraction of preliminary least squares esti-
mates computed without AR terms to be used. Note that
out of range values are set to “s=1”. Specifying “s=0” ini-
tializes coefficients to zero. By default EViews uses “s=1”.
Does not apply to coefficients for AR terms which are
instead set to EViews determined default values.
numericderiv /
-numericderiv
[Do / do not] use numeric derivatives only. If omitted,
EViews will follow the global default.
fastderiv / -fastderiv [Do / do not] use fast derivative computation. If omitted,
EViews will follow the global default.
showopts /
-showopts
[Do / do not] display the starting coefficient values and
estimation options in the estimation output.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-421-320.jpg)
![416—Chapter 12. Command Reference
Estimates an equation using Mixed Data Sampling (MIDAS) regression.
MIDAS regression is an estimation technique which allows for data sampled at different fre-
quencies to be used in the same regression.
Syntax
midas(options) y x1 [x2 x3 ...] @ z1pagez1 [z2pagez2 ...]
where y, x1, etc., are the dependent and explanatory variables in the current page frequency,
and z1pagez1 and z2pagez2 are the high frequency variable pageseries specification.
You may not include ARMA terms in a MIDAS regression.
Options
midas Interactive Use Commands
midwgt=arg MIDAS weight method: step function(“step”), normalized
exponential Almon (“expalmon”), normalized beta func-
tion (“beta”), or the default Almon/PDL weighting
(“almon”).
lag=arg Method for specifying the number of lags of the high fre-
quency regressor to use: lag selection (“auto”), fixed
(“fixed”). The default is “lag=fixed”.
maxlag=arg Maximum number of lags of the high frequency regressor
to use when using lag selection. For use when “lag=auto”.
The default value is 4.
fixedlag=arg Fixed number of lags of the high frequency regressor to
use. For use when “lags=fixed”. The default value is 4.
steps=integer Stepsize (number of high frequency periods to group). For
use when “midwgt=step”.
polynomial=integer Polynomial degree. For use when Almon/PDL weighting is
employed.
beta=arg Beta function restriction: none (“none”), trend coefficient
equals 1 (“trend”), endpoints coefficient equals 0 (“end-
point”), both trend and endpoints restriction (“both”).
For use when “midwgt=beta”. The default is
“beta=none”.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-423-320.jpg)
![midas—417
optmethod = arg Optimization method for nonlinear estimation: “bfgs”
(BFGS); “newton” (Newton-Raphson), “opg” or “bhhh”
(OPG or BHHH), “hybrid” (initial BHHH followed by
BFGS).
Hybrid is the default method.
optstep = arg Step method for nonlinear estimation: “marquardt” (Mar-
quardt); “dogleg” (Dogleg); “linesearch” (Line search).
Marquardt is the default method.
cov=arg Covariance method for nonlinear models: “ordinary”
(default method based on inverse of the estimated informa-
tion matrix), “huber” or “white” (Huber-White sandwich).
covinfo = arg Information matrix method for nonlinear models: “opg”
(OPG); “hessian” (observed Hessian).
nodf Do not perform degree of freedom corrections in computing
coefficient covariance matrix. The default is to use degree
of freedom corrections.
m=integer Set maximum number of iterations.
c=scalar Set convergence criterion. The criterion is based upon the
maximum of the percentage changes in the scaled coeffi-
cients. The criterion will be set to the nearest value
between 1e-24 and 0.2.
s Use the current coefficient values in estimator coefficient
vector as starting values in nonlinear estimation (see also
param (p. 453) of the Command and Programming Ref-
erence).
s=number Determine starting values for nonlinear estimation.. Specify
a number between zero and one representing the fraction
of preliminary EViews chosen values. Note that out of
range values are set to “s=1”. Specifying “s=0” initializes
coefficients to zero. By default EViews uses “s=1”.
showopts /
-showopts
[Do / do not] display the starting coefficient values and
estimation options in the estimation output.
coef=arg Specify the name of the coefficient vector (if specified by
list); the default behavior is to use the “C” coefficient vec-
tor.
prompt Force the dialog to appear from within a program.
p Print estimation results.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-424-320.jpg)
![418—Chapter 12. Command Reference
Examples
midas(fixedlag=9, midwgt=beta, beta=endpoint) realgdp c realgdp(-
1) @ monthlypageemp(-5)
estimates a MIDAS beta weight specification using the low frequency dependent variable
REALGDP and regressors C and REALGDP(-1), and 9 beta weighted lags of EMP(-5) from
the “monthlypage” workfile page. The beta weight function places zero restrictions on the
endpoint coefficient.
midas(maxlag=12, lag=auto) realgdp c realgdp(-1) @
monthlypageemp(-5)
estimates the same equation using PDL/Almon weights. The number of lags is chosen auto-
matically with a maximum of 12 lags.
Cross-references
Chapter 1. “MIDAS Regression,” on page 297 of the User’s Guide II discusses the specifica-
tion and estimation of MIDAS regression models in EViews.
Opens a program file, or text (ASCII) file.
This command should be used to open program files or text (ASCII) files for editing.
You may also use the command to open workfiles or databases. This use of the open com-
mand for this purposes is provided for backward compatibility. We recommend instead that
you use the new commands wfopen (p. 515) and pageload (p. 440) to open a workfile, and
dbopen (p. 344) to open databases.
Syntax
open(options) [path]file_name
You should provide the name of the file to be opened including the extension (and option-
ally a path), or the file name without an extension but with an option identifying its type.
Specified types always take precedence over automatic type identification. If a path is not
provided, EViews will look for the file in the default directory.
Files with the “.PRG” extension will be opened as program files, unless otherwise specified.
Files with the “.TXT” extension will be opened as text files, unless otherwise specified.
For backward compatibility, files with extensions that are recognized as database files are
opened as EViews databases, unless an explicit type is specified. Similarly, files with the
extensions “.WF” and “.WF1”, and foreign files with recognized extensions will be opened
as workfiles, unless otherwise specified.
open Object Container, Data, and File Commands || Programming Commands](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-425-320.jpg)
![420—Chapter 12. Command Reference
Since the objective and controls are defined by a standard EViews subroutine, the arguments
of the subroutine may correspond to numbers, strings, and various EViews objects such as
series, vectors, scalars.
Options
Optimization Objective and Controls
The default optimization objective is to maximize the first argument of the subroutine. You
may use the following optimization options to change the optimization objective and to
specify the coefficients (control parameters):
max [=integer] Maximize the subroutine objective or sum of the values of
the subroutine objective (default).
By default the first argument of the subroutine is used as
the maximization objective. You may change the objective
to another argument by specifying an integer argument
location.
min [=integer] Minimize the subroutine objective or sum of the values of
the subroutine objective.
By default the first argument of the subroutine is used as
the minimization objective. You may change the objective
to another argument by specifying an integer argument
location.
ls [=integer] Perform least squares minimization of the sum of squared
values of the subroutine objective. (The objective argu-
ment cannot be a scalar value when using this option.)
By default the first argument of the subroutine is used as
the minimization objective. You can change the objective to
another argument by specifying an integer argument loca-
tion.
ml [=integer] Perform maximum likelihood estimation of the sum of the
values of the subroutine objective. (The objective argu-
ment cannot be a scalar value when using this option.)
By default the first argument of the subroutine is used as
the maximization objective. You can change the objective
to another argument by specifying an integer argument
location.
Note that the “ml” option specifies the same optimization
as when using the “max” option, but permits a different set
of Hessian matrix choices.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-427-320.jpg)
![426—Chapter 12. Command Reference
Cross-references
See Appendix A. “Global Options,” beginning on page 813 of User’s Guide I for discussion of
the global options.
See also optsave (p. 424).
Estimation of ordered dependent variable models.
Syntax
equation name.ordered(options) y x1 [x2 x3 ...]
equation name.ordered(options) specification
Options
ordered Interactive Use Commands
d=arg
(default=“n”)
Specify likelihood: normal likelihood function, ordered pro-
bit (“n”), logistic likelihood function, ordered logit (“l”),
Type I extreme value likelihood function, ordered Gompit
(“x”).
optmethod =
arg
Optimization method: “bfgs” (BFGS); “newton” (Newton-
Raphson), “opg” or “bhhh” (OPG or BHHH), “legacy”
(EViews legacy).
Newton-Raphson is the default method.
optstep = arg Step method: “marquardt” (Marquardt); “dogleg” (Dog-
leg); “linesearch” (Line search).
Marquardt is the default method.
cov=arg Covariance method: “ordinary” (default method based on
inverse of the estimated information matrix), “huber” or
“white” (Huber-White sandwich method)., “glm” (GLM
method)..
covinfo = arg Information matrix method: “opg” (OPG); “hessian”
(observed Hessian).
(Applicable when non-legacy “optmethod=”.)
h Huber-White quasi-maximum likelihood (QML) standard
errors and covariances.
(Legacy option Applicable when “optmethod=legacy”).
m=integer Set maximum number of iterations.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-433-320.jpg)
![ordered—427
If you choose to employ user specified starting values, the parameters corresponding to the
limit points must be in ascending order.
Examples
ordered(d=l,cov=huber) y c wage edu kids
estimates an ordered logit model of Y on a constant, WAGE, EDU, and KIDS with QML stan-
dard errors. This command uses the default quadratic hill climbing algorithm.
param c(1) .1 c(2) .2 c(3) .3 c(4) .4 c(5).5
equation eq1.binary(s) y c x z
coef betahat = @coefs
estimates an ordered probit model of Y on a constant, X, and Z from the specified starting
values. The estimated coefficients are then stored in the coefficient vector BETAHAT.
Cross-references
See “Ordered Dependent Variable Models” on page 334 of the User’s Guide II for additional
discussion.
See also binary (p. 299).
See Equation::ordered (p. 135) for the equivalent equation object command.
c=scalar Set convergence criterion. The criterion is based upon the
maximum of the percentage changes in the scaled coeffi-
cients. The criterion will be set to the nearest value
between 1e-24 and 0.2.
s Use the current coefficient values in “C” as starting values
(see also param (p. 453)).
s=number Specify a number between zero and one to determine start-
ing values as a fraction of preliminary EViews default val-
ues (out of range values are set to “s=1”).
showopts /
-showopts
[Do / do not] display the starting coefficient values and
estimation options in the estimation output.
coef=arg Specify the name of the coefficient vector (if specified by
list); the default behavior is to use the “C” coefficient vec-
tor.
prompt Force the dialog to appear from within a program.
p Print results.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-434-320.jpg)
![428—Chapter 12. Command Reference
Redirect printer output.
You may specify that any procedure that would normally send output to the printer puts out-
put in a text, Rich Text Format (RTF), or comma-separated value (CSV) file, in a spool
object, or into frozen table or graph objects in the current workfile.
Syntax
output[(f)] base_name
output(options) [path]file_name
output[(s)] spool_name
output off
By default, the output command redirects the output into frozen objects. You should supply
a base name after the output keyword. Each subsequent print command will create a new
table or graph object in the current workfile, using the base name and an identifying num-
ber. For example, if you supply the base name of “OUT”, the first print command will gener-
ate a table or graph named OUT01, the second print command will generate OUT02, and so
on.
You can also use the optional settings, described below, to redirect table and text output to a
text, RTF, or CSV file, or all output (including graphs) to an RTF file. If you elect to redirect
output to a file, you must specify a filename.
You can use the “s” option, described below, to redirect all output to a spool object.
When followed by the optional keyword off, the output command turns off output redirec-
tion. Subsequent print commands will be directed to the printer.
Options
output Programming Commands
f Redirect all output to frozen objects in the default workfile,
using base_name.
t Redirect table and text output to a text file. Graphic output
will still be sent to the printer.
r Redirect all output to an Rich Text Format (RTF) file.
v Redirect all output to an comma-separated value (CSV) file.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-435-320.jpg)
![430—Chapter 12. Command Reference
Append contents of the specified workfile page to the active workfile page.
Syntax
pageappend(options) wfname[pgname] [object_list]
where wfname is the name of a workfile that is currently in memory. You may optionally
provide the name of a page in wfname that you wish to used as a source, and the list of
objects to be read from the source workfile page. If no wfname is provided, EViews will use
the default page in the source workfile.
The command appends the contents of the source page to the active page in the default
workfile. The target page is first unstructured (if necessary) and its range is expanded to
encompass the combined range of the sample from the source workfile page, and the desti-
nation page.
The default behavior is to append all series and alpha objects (but not other types) from the
source page, but the optional object_list may be provided to specify specific series, or to
specify objects other than series or alpha objects to be included. Command options may also
be used to modify the list of objects to be included.
Note that since this operation is performed in place, the original workfile page cannot be
recovered. We recommend that you consider backing up your original page using pagecopy
(p. 432).
Options
pageappend Object Container, Data, and File Commands
smpl=smpl_spec Specifies an optional sample identifying which observa-
tions from the source page are to be appended. Either pro-
vide the sample range in double quotes or specify a named
sample object. The default is “@all”.
allobj Specifies that all objects (including non-series and non-
alpha objects) should be appended. For objects other than
series and alphas, appending involves simply copying the
objects from the source page to the destination page. This
option may not be used with an explicit object_list specifi-
cation.
match Specifies that only series and alphas in the append page
that match series and alphas of the same name in the
active page should be appended. This option may not be
used with “allobj” or with an explicit object_list specifica-
tion.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-437-320.jpg)
![432—Chapter 12. Command Reference
place, you may wish to backup your original page (see pagecopy (p. 432)) prior to contract-
ing.
Examples
pagecontract if income<50000 and race=2
removes all observations with INCOME values greater than or equal to 50000 and RACE not
equal to 2.
pagecontract 1920 1940 1945 2000
removes observations for the years 1941 to 1944.
Cross-references
See “Contracting a Workfile” on page 284 of User’s Guide I for discussion.
See also pagecopy (p. 432).
Copies all or part of the active workfile page to a new workfile, or to a new page within the
default workfile.
Syntax
pagecopy(options) [object_list]
where the optional object_list specifies the workfile objects to be copied. If object_list is not
provided, all objects will be copied subject to the option restrictions discussed below.
If copying objects to a new page within the default workfile, you may choose to copy series
objects (series, alphas, and links) by value or by link (by employing the “bylink” option). If
you elect to copy by value, links in the source page will converted to ordinary series and
alpha objects when they are copied. If you copy by link, series and alpha objects in the
source page are converted to links when copied. The default is to copy by value.
If you copy objects to a new workfile, data objects must be copied by value.
pagecopy Object Container, Data, and File Commands](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-439-320.jpg)
![434—Chapter 12. Command Reference
will instead create a page ALLLINK in the existing workfile, and will copy all series objects
by creating links in the new page.
pagecopy(page=partcopy, bylink, smpl="1950 2000 if
gender=""male""") a* *z
will create a new page named PARTCOPY in the existing workfile containing the specified
subsample of observations, and will copy all series objects in the current page beginning
with the letter “A” or ending with the letter “Z”. The objects will be copied by creating links
in the new page.
pagecopy(page=rndcopy, smpl="1950 2000 if gender=""male""",
rndobs=200, dataonly, nolinks)
creates a new workfile and page RNDCOPY containing a 200 observation random sample
from the specified subsample. Series and alpha objects only will be copied by value from the
source page.
Cross-references
See “Copying from a Workfile” on page 284 of User’s Guide I for discussion.
See also pageappend (p. 430).
Create a new page in the default workfile. The new page becomes the active page.
Syntax
pagecreate(options) freq[(subperiod_opts)] start_date end_date [num_cross_sections]
pagecreate(options) u num_observations
pagecreate(id_method[,options]) id_list [@srcpage page_list]
pagecreate(idcross[,options]) id1 id2 [@srcpage page1 page2]
pagecreate(idcross[,options]) id1 @range(freq, start_date, end_date) [@srcpage
page1]
These different forms of the pagecreate command encompass three distinct approaches to
creating a new workfile page: (1) regular frequency description or unstructured data descrip-
tion; (2) using the union or intersection of unique values from one or more ID series in exist-
ing workfile pages; (3) using the cross of unique values from two identifier series or from an
identifier series and a date range. Each of these approaches is described in greater detail
below.
pagecreate Object Container, Data, and File Commands](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-441-320.jpg)
![pagecreate—435
Regular Frequency or Unstructured Description
The first two forms of the command permit you to create a new workfile page using a regu-
lar frequency or unstructured description:
• pagecreate(options) freq[(subperiod_opts)] start_date end_date [num_cross_sections]
• pagecreate(options) u num_observations
The first form of the command should be employed to create a regular frequency page with
the specified frequency, start, and end date. If you include the optional argument
num_cross_sections, EViews will create a balanced panel page using integer identifiers for
each of the cross-sections. Note that more complex panel structures may be defined using
pagestruct (p. 448).
The second form of the command creates an unstructured workfile with the specified num-
ber of observations.
Note that these forms of the command are analogous to wfcreate (p. 510) except that
instead of creating a new workfile, we create a new page in the default workfile.
The freq argument should be specified using one of the following forms:
Sec[opt], 5Sec[opt],
15Sec[opt],
30Sec[opt]
Seconds in intervals of: 1, 5, 15, or 30 seconds, respec-
tively. You may optionally specify days of the week and
start and end times during the day using the opt parameter.
See explanation of subperiod options below.
Min[opt],
2Min[opt],
5Min[opt],
10Min[opt],
15Min[opt],
20Min[opt],
30Min[[opt]
Minutes in intervals of: 1, 2, 5, 10, 15, 20, or 30 minutes,
respectively. You may optionally specify days of the week
and start and end times during the day using the opt
parameter. See explanation of subperiod options below.
H[opt], 2H[opt],
4H[opt], 6H[opt],
8H[opt], 12H[[opt]
Hours in intervals of: 1, 2, 4, 6, 8, or 12 hours, respectively.
You may optionally specify days of the week and start and
end times during the day using the opt parameter. See
explanation of subperiod options below.
D(s, e) Daily with arbitrary days of the week. Specify the first and
last days of the week with integers s and e, where Monday
is given the number 1 and Sunday is given the number 7.
(Note that the “D” option used to specify a 5-day frequency
in versions prior to EViews 7).
D5 or 5 Daily with five days per week, Monday through Friday.
D7 or 7 Daily with seven days per week.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-442-320.jpg)
![pagecreate—437
You may also include a time with the start and end date parameters to specify partial days at
the beginning or end of the workfile. For example,
pagecreate(wf=strhours) 30MIN(1-6, 8AM-5PM) 1/3/2000 10AM
12/30/2000 2PM
creates the same workfile page as above, but limits the first day, 1/3/2000, to 10AM - 5PM
and the last day, 12/30/2000, to 8AM - 2PM.
Unique Values from a Set of Identifier Series
The next form of the command allows for creating pages from the unique values of one or
more identifier series found in one or more workfile pages:
• pagecreate(id_method[,options]) identifier_list [@srcpage page_list]
The identifier_list should include one or more ID series. If more than one ID series is pro-
vided, EViews will use the values that are unique across all of the series. If you wish to cre-
ate a page with a date structure, you should specify one of your identifiers using the special
“@DATE” keyword identifier, enclosing the series (or the date ID component series) inside
parentheses. If you wish to use the date ID values from the source workfile page, you may
use the “@DATE” keyword without arguments.
The id_method describes how to handle unique ID values that differ across multiple pages:
If the optional source page or list of source pages is not provided, EViews will use the
default workfile page. Note that if a single workfile page is used, the two ID methods yield
identical results.
Cross of Unique Values from Two Identifier Series or from an Identifier Series and a Date Range
The last two forms of the command create a new page by crossing the unique values in two
ID series located in one or more pages, or by crossing an ID series from one page with a date
range. First, you may specify a pair of identifiers, and optionally source pages where they
are located,
• pagecreate(idcross[,options]) id1 id2 [@srcpage page1 page2]
You may instruct EViews to create a date structured page by specifying one of your two iden-
tifiers using a “@DATE” specification as described above.
id Use the observed values of the series in the identifier_list in
specified page.
idunion Use the union of the observed values of the series in the
identifier_list in the specified pages.
idintersect Use the intersection of the observed values of the series in
the identifier_list in the specified pages.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-444-320.jpg)
![438—Chapter 12. Command Reference
Alternately, you may provide a single identifier and a range specification using the
“@RANGE” keyword with a freq, start_date, and end_date, and optionally, the location of
the identifier series.
• pagecreate(idcross[,options]) id1 @range(freq, start_date, end_date) [@srcpage
page1]
Options
Examples
Regular Frequency or Unstructured Description
The two commands:
pagecreate(page=annual) a 1950 2005
pagecreate(page=unstruct) u 1000
create new pages in the existing workfile. The first page is an annual page named ANNUAL,
containing data from 1950 to 2005; the second is a 1000 observation unstructured page
named UNSTRUCT.
pagecreate(page=mypanel) a 1935 1954 10
creates a new workfile page named MYPANEL, containing a 10 cross-section annual panel
for the years 1935 to 1954.
pagecreate(page=fourday) D(1,4) 1/3/2000 12/31/2000
specifies a daily workfile page from January 3, 2000 to December 31, 2000, including only
Monday through Thursday. The day range may be delimited by either a comma or a dash,
such that
pagecreate(wf=fourday) D(1-4) 1/3/2000 12/31/2000
is equivalent to the previous command.
smpl=smpl_spec Specifies an optional sample identifying which observa-
tions to use when creating a page using the id_method
option. Either provide the sample range in double quotes or
specify a named sample object. The default is “@all”.
When multiple source workfiles are involved, the specified
sample must be valid for all workfiles.
page=page_name Optional name for the newly created page. If not provided,
EViews will use the next available name of the form “Unti-
tled##”, where ## is a number.
wf=wf_name Optional name for the new workfile. If not provided,
EViews will create a new page in the default workfile.
prompt Force the dialog to appear from within a program.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-445-320.jpg)
![440—Chapter 12. Command Reference
pagecreate(idcross,page=AnnualPanel) id1 @date(year) @srcpage
page1 page2
You may also specify the cross of an identifier with a date range:
pagecreate(idcross,page=QuarterlyPanel) id1 @range(q, 1950, 2006)
@srcpage page1
creates a quarterly panel page named QUARTERLYPANEL using the cross of ID1 taken from
PAGE1, and quarterly observations from 1950q1 to 2006q4.
Cross-references
See “Creating a Workfile,” on page 42 of User’s Guide I for discussion.
See also wfcreate (p. 510) and pagedelete (p. 440).
Delete the named pages from the default workfile.
Syntax
pagedelete [pgname1 pgname2 pgname3...]
By default pagedelete will delete the currently active page in the default workfile. You may
specify other pages to delete by listing them, in a space delimited list, after the pagedelete
command.
Examples
pagedelete page1 page2
Cross-references
See also pagecreate (p. 434).
Load one or more new pages in the default workfile.
Syntax
pageload [path]workfile_name [page1] [page2] [...]
pageload(options) source_description [@keep keep_list] [@drop drop_list] [@keep-
map keepmap_list] [@dropmap dropmap_list] [@selectif condition]
pageload(options)source_description table_description [@keep keep_list] [@drop
drop_list] [@keepmap keepmap_list] [@dropmap dropmap_list] [@selectif con-
dition]
pagedelete Object Container, Data, and File Commands
pageload Object Container, Data, and File Commands](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-447-320.jpg)
![442—Chapter 12. Command Reference
Rename the specified workfile page.
Syntax
pagerename old_name new_name
renames the old_name page in the default workfile to new_name. Page names are case-
insensitive for purposes of comparison, even though they are displayed using the input case.
Examples
pagerename Page1 StateByYear
Cross-references
See also pagecreate (p. 434).
Save the active page in the default workfile as an EViews workfile (.WF1 file) or as a for-
eign data source.
Syntax
pagesave(options) [path]filename
pagesave(options) source_description [@keep keep_list] [@drop drop_list] [@keep-
map keepmap_list] [@dropmap dropmap_list] [@smpl smpl_spec]
pagesave(options) source_description table_description [@keep keep_list] [@drop
drop_list] [@keepmap keepmap_list] [@dropmap dropmap_list] [@smpl
smpl_spec]
The command saves the active page in the specified directory using filename. By default, the
page is saved as an EViews workfile, but options may be used to save all or part of the page
in a foreign file or data source. See wfopen (p. 515) for details on the syntax of source_de-
scriptions and table_descriptions. Note, in particular, that you may use the byrow table_de-
scription for Excel 2007 files to instruct EViews to save the series by row (instead of the
standard by column).
pagerename Object Container, Data, and File Commands
pagesave Object Container, Data, and File Commands](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-449-320.jpg)
![pagestack—445
Examples
pageselect page2
changes the active page to PAGE2.
Cross-references
See also wfselect (p. 532).
Create a panel structured workfile page using series, alphas, or links from the default
workfile page (convert repeated series to repeated observations).
Series in the new panel workfile may be created by stacking series, alphas, and links whose
names contain a pattern (series with names that differ only by a “stack identifier”), or by
repeating a single series, alpha, or link, for each value in a set of stack identifiers.
Syntax
pagestack(options) stack_id_list [@ series_to_stack]
pagestack(options) pool_name [@ series_to_stack]
pagestack(options) series_name_pattern [@ series_to_stack]
The resulting panel workfile will use the identifiers specified in one of the three forms of the
command:
• stack_id_list includes a list of the ID values (e.g., “US UK JPN”).
• pool_name is the name of a pool object that contains the ID values.
• series_name_pattern contains an expression from which the ID values may be deter-
mined. The pattern should include the “?” character as a stand in for the parts of the
series names containing the stack identifiers. For example, if “CONS?” is the
series_name_pattern, EViews will find all series with names beginning with “CONS”
and will extract the IDs from the trailing parts of the observed names.
The series_to_stack list may contain two types of entries: stacked series (corresponding to
sets of series, alphas, and links whose names contain the stack IDs) and simple series (other
series, alphas, and links).
To stack a set of series whose names differ only by the stack IDs, you should enter an
expression that includes the “?” character in place of the IDs. You may list the names of a
single stacked series (e.g., “GDP?” or “?CONS”), or you may use expressions containing the
wildcard character “*” (e.g., “*?” and “?C*”) to specify multiple series.
pagestack Object Container, Data, and File Commands](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-452-320.jpg)
![448—Chapter 12. Command Reference
Cross-references
For additional discussion, see “Stacking a Workfile” on page 297 in User’s Guide I. See also
pageunstack (p. 451).
Assign a structure to the active workfile page.
Syntax
pagestruct(options) [id_list]
pagestruct(options) *
where id_list is an (optional) list of ID series. The “*” may be used as shorthand for the indi-
ces currently in place.
If an id_list is provided, EViews will attempt to auto determine the workfile structure. Auto-
determination may be overridden through the use of options.
If you do not provide an id_list, the workfile will be restructured as a regular frequency
workfile. In this case, either the “none” or the “freq=” and “start=” options described
below must be provided.
Options
pagestruct Object Container, Data, and File Commands
none Remove the existing workfile structure.
freq= arg Specifies a regular frequency; if not provided EViews will
auto-determine the frequency. The frequency may be speci-
fied as “a” (annual), “s” (semi-annual), “q” (quarterly),
“m” (monthly), “w” (weekly), “d” (5-day daily), “7” (7-
day daily), or “u” (unstructured/undated).
start=arg Start date of the regular frequency structure; if not speci-
fied, defaults to “@FIRST”. Legal values for arg are
described below.
end=arg End date of the regular frequency structure; if not specified,
defaults to “@LAST”. Legal values for arg are described
below.
regular, reg When used with a date ID, this option informs EViews to
insert observations (if necessary) to remove gaps in the
date series so that it follows the regular frequency calendar.
The option has no effect unless a date index is specified.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-455-320.jpg)
![pageunstack—451
Cross-references
See Chapter 8. “Series Links,” on page 223 of the User’s Guide I for a description of link
objects, and “Auto-Updating Series” on page 193 of the User’s Guide I for a discussion of
auto-updating series.
See also Link::link (p. 352) and Link::linkto (p. 353) in the Object Reference.
See unlink (p. 501) and wfunlink (p. 533) for object and workfile based unlinking, respec-
tively.
Unstack workfile page (convert repeated observations to repeated series).
Create a new workfile page by taking series objects (series, alphas, or links) in the default
workfile page and breaking them into multiple series (or alphas), one for each distinct value
found in a user supplied list of series objects. Typically used on a page with a panel struc-
ture.
Syntax
pageunstack(options) stack_id obs_id [@ series_to_unstack]
where stack_id is a single series containing the unstacking ID values used to identify the
individual unstacked series, obs_id is a series containing the observation IDs, and
series_to_unstack is an optional list of series objects to be copied to the new workfile.
Options
pageunstack Object Container, Data, and File Commands
namepat
=name_pattern
Specifies the pattern from which unstacked series names
are constructed, where “*” indicates the original series
name and “?” indicates the stack ID.
By default the name_pattern is “*?”, indicating, for exam-
ple, that if we have the IDs “US”, “UK”, “JPN”, the
unstacked series corresponding to the series GDP should be
named “GDPUS”, “GDPUK”, “GDPJPN” in the unstacked
workfile page.
wf=wf_name Optional name for the new workfile. If not provided,
EViews will create a new page in the default workfile.
page=page_name Optional name for the newly created page. If not provided,
EViews will use the next available name of the form “Unti-
tled##”, where ## is a number.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-458-320.jpg)
![plot—453
creates the series US_GDP, UK_GDP, JPN_GDP, etc.
Cross-references
For additional discussion and examples, see “Unstacking a Workfile” on page 291 of the
User’s Guide I. See also pagestack (p. 445).
Set parameter values.
Allows you to set the current values of coefficient vectors. The command may be used to
provide starting values for the parameters in nonlinear least squares, nonlinear system esti-
mation, and (optionally) ARMA estimation.
Syntax
param coef_name1 number1 [coef_name2 number2 coef_name3 number3…]
List, in pairs, the names of the coefficient vector and its element number followed by the
corresponding starting values for any of the parameters in your equation.
Examples
param c(1) .2 c(2) .1 c(3) .5
resets the first three values of the coefficient vector C.
coef(3) beta
param beta(2) .1 beta(3) .5
The first line declares a coefficient vector BETA of length 3 that is initialized with zeros. The
second line sets the second and third elements of BETA to 0.1 and 0.5, respectively.
Cross-references
See “Starting Values” on page 48 of the User’s Guide II for a discussion of setting initial val-
ues in nonlinear estimation.
Line graph.
Provided for backward compatibility. See line (p. 924).
param Global Commands
plot Object Creation Commands](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-460-320.jpg)
![454—Chapter 12. Command Reference
Sends views of objects to the default printer.
Syntax
print(options) object1 [object2 object3 …]
print(options) object_name.view_command
print should be followed by a list of object names or a view of an object to be printed. The
list of names must be of the same object type. If you do not specify the view of an object,
print will print the default view for the object.
Options
The default orientation is set by clicking on Print Setup.
Examples
print gdp log(gdp) d(gdp) @pch(gdp)
sends a table of GDP, log of GDP, first difference of GDP, and the percentage change of GDP
to the printer.
print graph1 graph2 graph3
prints three graphs on a single page.
To merge the three graphs, realign them in one row, and print in landscape orientation, you
may use the commands:
graph mygra.merge graph1 graph2 graph3
mygra.align(3,1,1)
print(l) mygra
To estimate the equation EQ1 and send the output view to the printer.
print eq1.ls gdp c gdp(-1)
Cross-references
See “Print Setup,” beginning on page 833 of the User’s Guide II for a discussion of print
options and the Print Setup dialog.
See output (p. 428) for print redirection.
print Command Actions
p Print in portrait orientation.
l Print in landscape orientation.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-461-320.jpg)
![qreg—455
Estimation of binary dependent variable models with normal errors.
Equivalent to “binary(d=n)”.
See binary (p. 299).
Declare a program.
Syntax
program [path]prog_name
Enter a name for the program after the program keyword. If you do not provide a name,
EViews will open an untitled program window. Programs are text files, not objects.
Examples
program runreg
opens a program window named RUNREG which is ready for program editing.
Cross-references
See Chapter 6. “EViews Programming,” on page 117 of the User’s Guide I for further details,
and examples of writing EViews programs.
See also open (p. 418).
Estimate a quantile regression specification.
Syntax
qreg(options) y x1 [x2 x3 ...]
qreg(options) linear_specification
probit Interactive Use Commands
program Programming Commands
qreg Interactive Use Commands](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-462-320.jpg)
![qreg—457
btreps=integer
(default=100)
Number of bootstrap repetitions
btseed=positive
integer
Seed the bootstrap random number generator.
If not specified, EViews will seed the bootstrap random
number generator with a single integer draw from the
default global random number generator.
btrnd=arg
(default=“kn” or
method previously
set using rndseed
(p. 463)).
Type of random number generator for the bootstrap:
improved Knuth generator (“kn”), improved Mersenne
Twister (“mt”), Knuth’s (1997) lagged Fibonacci generator
used in EViews 4 (“kn4”) L’Ecuyer’s (1999) combined mul-
tiple recursive generator (“le”), Matsumoto and
Nishimura’s (1998) Mersenne Twister used in EViews 4
(“mt4”).
btobs=integer Number of observations for bootstrap subsampling (when
“bsmethod=pair”).
Should be significantly greater than the number of regres-
sors and less than or equal to the number of observations
used in estimation. EViews will automatically restrict val-
ues to the range from the number of regressors and the
number of estimation observations.
If omitted, the bootstrap will use the number of observa-
tions used in estimation.
btout=name (optional) Matrix to hold results of bootstrap simulations.
k=arg
(default=“e”)
Kernel function for sparsity and coefficient covariance
matrix estimation (when “spmethod=kernel”): “e” (Epan-
echnikov), “r” (Triangular), “u” (Uniform), “n” (Normal–
Gaussian), “b” (Biweight–Quartic), “t” (Triweight), “c”
(Cosinus).
m=integer Maximum number of iterations.
s Use the current coefficient values in “C” as starting values
(see also param (p. 453)).
s=number (default
=0)
Determine starting values for equations. Specify a number
between 0 and 1 representing the fraction of preliminary
least squares coefficient estimates.
Note that out of range values are set to the default.
showopts /
-showopts
[Do / do not] display the starting coefficient values and
estimation options in the estimation output.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-464-320.jpg)
![458—Chapter 12. Command Reference
Examples
qreg y c x
estimates the default least absolute deviations (median) regression for the dependent vari-
able Y on a constant and X. The estimates use the Huber Sandwich method for computing
the covariance matrix, with individual sparsity estimates obtained using kernel methods.
The bandwidth uses the Hall and Sheather formula.
qreg(quant=0.6, cov=boot, btmethod=mcmba) y c x
estimates the quantile regression for the 0.6 quantile using MCMB-A bootstrapping to obtain
estimates of the coefficient covariance matrix.
Cross-references
See Chapter 8. “Quantile Regression,” on page 497 of the User’s Guide II for a discussion of
the quantile regression.
Reset the workfile range for a regular frequency workfile.
No longer supported. See the replacement command pagestruct (p. 448).
Import data from a foreign disk file into series.
May be used to import data into an existing workfile from a text, Excel, or Lotus file on disk.
Unless you need to merge data into an existing workfile page, we recommend that you use
the more powerful, easy-to-use tools for reading data (see “Creating a Workfile by Reading
from a Foreign Data Source” on page 47 of the User’s Guide I). See pageload (p. 440), and
wfopen (p. 515) for command details.
Syntax
read(options) [path]file_name name1 [name2 name3 ...]
read(options) [path]file_name n
coef=arg Specify the name of the coefficient vector (if specified by
list); the default behavior is to use the “C” coefficient vec-
tor.
prompt Force the dialog to appear from within a program.
p Print estimation results.
range Object Container, Data, and File Commands
read Object Container, Data, and File Commands](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-465-320.jpg)
![460—Chapter 12. Command Reference
Options for spreadsheet (Lotus, Excel) files
Examples
read(t=dat,na=.) a:mydat.raw id lwage hrs
reads data from an ASCII file MYDAT.RAW in the A: drive. The data in the file are listed by
observation, the missing value NA is coded as a “.” (dot or period), and there are three
series, which are to be named ID, LWAGE, HRS (from left to right).
read(a2,s=sheet3) cps88.xls 10
reads data from an Excel file CPS88 in the default directory. The data are organized by obser-
vation, the upper left data cell is A2, and 10 series are read from a sheet named SHEET3
using names provided in the file.
read(a2, s=sheet2) "networkdr 1cps91.xls" 10
name Series names provided in file.
label=integer Number of lines between the header line and the data.
Must be used with the “name” option.
rect (default) /
norect
[Treat / Do not treat] file layout as rectangular.
skipcol =
integer
Number of columns to skip. Must be used with the “rect”
option.
skiprow =
integer
Number of rows to skip. Must be used with the “rect”
option.
comment=
symbol
Specify character/symbol to treat as comment sign. Every-
thing to the right of the comment sign is ignored. Must be
used with the “rect” option.
singlequote Strings are in single quotes, not double quotes.
dropstrings Do not treat strings as NA; simply drop them.
negparen Treat numbers in parentheses as negative numbers.
allowcomma Allow commas in numbers (note that using commas as a
delimiter takes precedence over this option).
currency=
symbol
Specify symbol/character for currency data.
t Read data organized by series. Default is to read by obser-
vation with series in columns.
letter_number
(default=“b2”)
Coordinate of the upper-left cell containing data.
s=sheet_name Sheet name for Excel 5–8 Workbooks.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-467-320.jpg)
![reset—461
reads the Excel file CPS91 from the network drive specified in the path.
Cross-references
See “Importing Data” on page 139 of the User’s Guide I for a discussion and examples of
importing data from external files.
See also write (p. 535).
Rename an object in the active workfile or database.
Syntax
rename old_name new_name [old_name1 new_name1 [old_name2 new_name2 [...]]]
After the rename keyword, list the pairs of old object names followed by the new names.
Note that the name specifications may include matching wildcard patterns.
Examples
rename temp_u u2
renames an object named TEMP_U as U2.
rename aa::temp_u aa::u2
renames the object TEMP_U to U2 in database AA.
rename a* b*
renames all objects beginning with the letter “A” to begin with the letter “B”.
rename a1 a2 b1 b2
renames A1 to A2 and B1 to B2.
Cross-references
See Chapter 4. “Object Basics,” on page 97 of the User’s Guide I for a discussion of working
with objects in EViews.
Compute Ramsey’s regression specification error test.
Syntax
reset(n, options)
You must provide the number of powers of fitted terms n to include in the test regression.
rename Object Utility Commands
reset Interactive Use Commands](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-468-320.jpg)
![464—Chapter 12. Command Reference
Examples
rndseed 123456
genr t3=@qtdist(rnd,3)
rndseed 123456
genr t30=@qtdist(rnd,30)
generates random draws from a t-distribution with 3 and 30 degrees of freedom using the
same seed.
Cross-references
See the list of available random number generators in “Statistical Distribution Functions” on
page 569 of the User’s Guide I.
At press time, further information on the improved seeds may be found on the web at the
following addresses:
Knuth generator: http://sunburn.stanford.edu/~knuth/news02.html#rng
Mersenne twister: http://www.math.keio.ac.jp/~matumoto/MT2002/emt19937ar.html
See also nrnd (p. 606), rnd (p. 608) and rndint (p. 462).
References
Knuth, D. E. (1997). The Art of Computer Programming, Volume 2, Semi-numerical Algorithms,
3rd edition, Reading, MA: Addison-Wesley Publishing Company. Note: the C implementa-
tion of the lagged Fibonacci generator is described in the errata to the 2nd edition,
downloadable from Knuth's web site.
L’Ecuyer, P. (1999). “Good Parameters and Implementations for Combined Multiple Recursive Ran-
dom Number Generators,” Operations Research, 47(1), 159-164
Matsumoto, M. and T. Nishimura (1998). “Mersenne Twister: A 623-Dimensionally Equidistrib-
uted Uniform Pseudo-Random Number Generator,” ACM Transactions on Modeling and
Computer Simulation, 8(1), 3-30.
Estimates an equation using robust least squares.
You may perform three different types of robust estimation: M-estimation, S-estimation and
MM-estimation.
Syntax:
robustls(options) y x1 [x2 x3…]
Enter the robustls keyword, followed by the dependent variable and a list of the regres-
sors.
robustls Equation Methods](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-471-320.jpg)
![run—467
See Chapter 4. “Robust Least Squares,” beginning on page 405 of User’s Guide II for discus-
sion.
Run a program.
The run command executes a program. The program may be located in memory or stored in
a program file on disk.
Syntax
run(options) [path]prog_name(prog_options) [%0 %1 …]
If you wish to pass one or more options to the program, you should enclose them in paren-
theses immediately after the filename. If the program has arguments, you should list them
after the filename.
EViews first checks to see if the specified program is in memory. If the program is not
located, EViews then looks for the program on disk in the current working directory, or in the
specified path. The program file should have a “.PRG” extension, which you need not spec-
ify in the prog_name.
Options
Examples
run(q) simul(h=2) x xhat
run Programming Commands
integer
(default=1)
Set maximum errors allowed before halting the program.
c Run program file without opening a window for display of
the program file.
verbose / quiet Verbose mode in which messages will be sent to the status
line at the bottom of the EViews window (slower execu-
tion), or quiet mode which suppresses workfile display
updates (faster execution).
v / q Same as [verbose / quiet].
ver4 / ver5 Execute program in [version 4 / version 5] compatibility
mode.
this=object_na
me
Set the _this object for the executed program. If omitted,
the executed program will inherit the _this object from
the parent program, or from the current active workfile
object when the exec command is issued from the com-
mand window.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-474-320.jpg)
![468—Chapter 12. Command Reference
quietly runs a program named “Simul.prg” from the default directory using options string
“h=2” and arguments %0=X and %1=XHAT.
Since run is a command, it may also be placed in a program file. You should note that if you
put the run command in a program file and then execute the program, EViews will stop
after executing the program referred to by the run command. For example, if you have a pro-
gram containing:
run simul
print x
the print statement will not be executed since execution will stop after executing the com-
mands in “Simul.prg”. If this behavior is not intended, you should consider using the exec
(p. 350) command or an include (p. 713) statement.
Cross-references
See “Executing a Program” on page 120 of the User’s Guide I for further details.
See also exec (p. 350) and include (p. 713).
Save current workfile to disk.
This usage is provided only for backward compatibility, as it has been replaced with the
equivalent wfsave (p. 529) command.
Syntax
save [path]file_name
Follow the keyword with a name for the file. If an explicit path is not specified, the file will
be stored in the default directory, as set in the File Locations global options.
Examples
save MyWorkfile
saves the current workfile with the name MYWORKFILE.WF1 in the default directory.
save c:dataMyWF1
saves the current workfile with the name MYWF1.WF1 in the specified directory.
Cross-references
See wfsave (p. 529).
save Object Container, Data, and File Commands](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-475-320.jpg)
![setcell—469
Seasonal adjustment.
The seas command carries out seasonal adjustment using either the ratio to moving aver-
age, or the difference from moving average technique.
EViews also performs Census X11 and X12 seasonal adjustment. For details, see
Series::x11 (p. 614) and Series::x12 (p. 616) in the Object Reference.
Syntax
seas(options) series_name name_adjust [name_fac]
List the name of the original series and the name to be given to the seasonally adjusted
series. You may optionally include an additional name for the seasonal factors. seas will
display the seasonal factors using the convention of the Census X11 program.
Options
Examples
seas(a) pass pass_adj pass_fac
seasonally adjusts the series PASS using the additive method, and saves the adjusted series
as PASS_ADJ and the seasonal factors as PASS_FAC.
Cross-references
See “Seasonal Adjustment” on page 418 of the User’s Guide I for a discussion of seasonal
adjustment methods.
See also seasplot (p. 948), Series::x11 (p. 614), and Series::x12 (p. 616) in the
Object Reference.
Insert contents into cell of a table.
The setcell command puts a string or number into a cell of a table.
seas Interactive Use Commands
m Multiplicative (ratio to moving average) method.
a Additive (difference from moving average) method.
prompt Force the dialog to appear from within a program.
setcell Table Commands](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-476-320.jpg)
![470—Chapter 12. Command Reference
Syntax
setcell(table_name, r, c, content[, "options"])
Options
Provide the following information in parentheses in the following order: the name of the
table object, the row number, r, of the cell, the column number, c, of the cell, a number or
string to put in the cell, and optionally, a justification and/or numerical format code. A
string of text must be enclosed in double quotes.
The justification options are:
The numerical format code determines the format with which a number in a cell is dis-
played; cells containing strings will be unaffected. The format code can either be a positive
integer, in which case it specifies the number of decimal places to be displayed after the dec-
imal point, or a negative integer, in which case it specifies the total number of characters to
be used to display the number. These two cases correspond to the Fixed decimal and Fixed
character fields in the number format dialog.
Note that when using a negative format code, one character is always reserved at the start of
a number to indicate its sign, and that if the number contains a decimal point, that will also
be counted as a character. The remaining characters will be used to display digits. If the
number is too large or too small to display in the available space, EViews will attempt to use
scientific notation. If there is insufficient space for scientific notation (six characters or less),
the cell will contain asterisks to indicate an error.
Examples
setcell(tab1, 2, 1, "Subtotal")
puts the string “Subtotal” in row 2, column 1 of the table object named TAB1.
setcell(tab1, 1, 1, "Price and cost", "r")
puts the a right-justify string “Price and cost” in row 1, column 1 of the table object named
TAB1.
Note that in general, that unless you wish to control the formatting, assignment statements
of the form
Mytable(1,1) = "hello"
are easier than using the default setcell statement:
Setcell(mytable,1,1,"hello")
c Center the text/number in the cell.
r Right-justify the text/number in cell.
l Left-justify the text/number in cell.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-477-320.jpg)
![472—Chapter 12. Command Reference
See also Table::setwidth (p. 797) and Table::setheight (p. 790) in the Object Refer-
ence.
Place a double horizontal line in a table.
Provided for backward compatibility. For a more general method of setting the line charac-
teristics and borders for a set of table cells, see the table proc Table::setlines (p. 792)
See also Table::setwidth (p. 797) and Table::setheight (p. 790) in the Object Refer-
ence.
Syntax
setline(table_name, r)
Options
Specify the name of the table and the row number r in which to place the horizontal line.
Examples
setline(tab3,8)
places a (double) horizontal line in the eighth row of the table object TAB3.
Cross-references
Chapter 3. “Working with Tables and Spreadsheets,” on page 57 of the User’s Guide I
describes table formatting using commands. See also “Table Objects” on page 743 of the
User’s Guide I for a discussion and examples of table formatting in EViews.
See Table::setlines (p. 792) in the Object Reference for more flexible line drawing tools.
Note that this command is supported primarily for backward compatibility. There is a more
extensive set of table procs for working with and customizing tables. See “Table Procs,” on
page 770 in the Object Reference.
Start the Windows command shell, optionally executing a command.
Syntax
shell(options) [arg1 arg2 arg3…]
See spawn (p. 480) for available options. By default, the Windows command shell will be
started in hidden mode with the exit code for success set to zero.
setline Table Commands
shell Programming Commands](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-479-320.jpg)
![show—473
Examples
shell mkdir c:newdir
makes a new directory “c:newdir”.
shell(out=flist) dir /b *.wf1
lists all workfiles in the current directory, saving output in a table named FLIST.
Cross-references
See spawn (p. 480) for details on spawning a new process.
Display objects.
The show command displays series or other objects on your screen. A scalar object is dis-
played in the status line at the bottom of the EViews window.
Syntax
show object_name.view_command
show object1 [object2 object3 ...]
The command show should be followed by the name of an object, or an object name with
an attached view.
For series and graph objects, show can operate on a list of names. The list of names must be
of the same type. show creates and displays an untitled group or multiple graph object.
Examples
genr x=nrnd
show x.hist
close x
generates a series X of random draws from a standard normal distribution, displays the his-
togram view of X, and closes the series window.
show wage log(wage)
opens an untitled group window with the spreadsheet view of the two series.
freeze(gra1) wage.hist
genr lwage=log(wage)
freeze(gra2) lwage.hist
show gra1 gra2
opens an untitled graph object with two histograms.
show Command Actions](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-480-320.jpg)
![474—Chapter 12. Command Reference
Cross-references
See “Object Commands” on page 17 for discussion.
Chapter 1. “Object View and Procedure Reference,” on page 2 in the Object Reference pro-
vides a complete listing of the views of the various objects.
See also close (p. 310) and do (p. 349).
Exponential smoothing.
Forecasts a series using one of a number of exponential smoothing techniques. By default,
smooth estimates the damping parameters of the smoothing model to minimize the sum of
squared forecast errors, but you may specify your own values for the damping parameters.
smooth automatically calculates in-sample forecast errors and puts them into the series
RESID.
Syntax
smooth(method) series_name smooth_name [freq]
You should follow the smooth keyword with the name of the series and a name for the
smoothed series. You must also specify the smoothing method in parentheses. The optional
freq may be used to override the default for the number of periods in the seasonal cycle. By
default, this value is set to the workfile frequency (e.g. — 4 for quarterly data). For undated
data, the default is 5.
Options
Smoothing method options
smooth Interactive Use Commands
s[,x] Single exponential smoothing for series with no trend. You
may optionally specify a number x between zero and one
for the mean parameter.
d[,x] Double exponential smoothing for series with a trend. You
may optionally specify a number x between zero and one
for the mean parameter.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-481-320.jpg)
![smooth—475
Other Options:
If you wish to set only some of the damping parameters and let EViews estimate the other
parameters, enter the letter “e” where you wish the parameter to be estimated.
If the number of seasons is different from the frequency of the workfile (an unusual case
that arises primarily if you are using an undated workfile for data that are not monthly or
quarterly), you should enter the number of seasons after the smoothed series name. This
optional input will have no effect on forecasts without seasonal components.
Examples
smooth(s) sales sales_f
smooths the SALES series by a single exponential smoothing method and saves the
smoothed series as SALES_F. EViews estimates the damping (smoothing) parameter and dis-
plays it with other forecast statistics in the SALES series window.
smooth(n,e,.3) tb3 tb3_hw
smooths the TB3 series by a Holt-Winters no seasonal method and saves the smoothed
series as TB3_HW. The mean damping parameter is estimated while the trend damping
parameter is set to 0.3.
smpl @first @last-10
smooth(m,.1,.1,.1) order order_hw
smpl @all
graph gra1.line order order_hw
show gra1
n[,x,y] Holt-Winters without seasonal component. You may
optionally specify numbers x and y between zero and one
for the mean and trend parameters, respectively.
a[,x,y,z] Holt-Winters with additive seasonal component. You may
optionally specify numbers x, y, and z, between zero and
one for the mean, trend, and seasonal parameters, respec-
tively.
m[,x,y,z] Holt-Winters with multiplicative seasonal component. You
may optionally specify numbers x, y, and z, between zero
and one for the mean, trend, and seasonal parameters,
respectively.
prompt Force the dialog to appear from within a program.
p Print a table of forecast statistics.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-482-320.jpg)
![sort—479
solve first looks for the specified model in the current workfile. If it is not present, solve
attempts to fetch a model file (.DBL) from the default directory or, if provided, the path
specified with the model name.
Options
solve can take any of the options available in Model::solveopt (p. 446) in the Object Ref-
erence.
Examples
solve mod1
solves the model MOD1 using the default solution method.
solve(m=500,e) nonlin2
solves the model NONLIN2 with an extended search of up to 500 iterations.
Cross-references
See Chapter 14. “Models,” on page 719 of the User’s Guide II for a discussion of models.
See also Model::model (p. 435), Model::msg (p. 436) and Model::solveopt (p. 446) in
the Object Reference.
Sort the current workfile page.
The sort command sorts all series in the workfile page on the basis of the values of one or
more of the series. For purposes of sorting, NAs are considered to be smaller than any other
value. By default, EViews will sort the series in ascending order. You may use options to
override the sort order.
EViews will first remove any workfile structures and then will sort the workfile using the
specified settings.
Syntax
sort(options) arg1 [arg2 arg3…]
List the name of the series or groups by which you wish to sort the workfile. If you list two
or more series, sort uses the values of the second series to resolve ties from the first series,
and values of the third series to resolve ties from the second, and so on.
Options
sort Object Container, Data, and File Commands
d sort in descending order.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-486-320.jpg)
![480—Chapter 12. Command Reference
Examples
sort(d) inc
sorts all series in the workfile in order of the INC series with the highest value of INC first.
NAs in INC (if any) will be placed at the bottom.
sort gender race wage
sorts all series in the workfile in order of the values of GENDER from low to high, with ties
resolved by ordering on the basis of RACE, with further ties resolved by ordering on the
basis of WAGE.
Cross-references
See “Sorting a Workfile” on page 305 of the User’s Guide I.
Spawn a new process.
Syntax
spawn(options) filename [arg1 arg2 arg3…]
Follow the keyword with a filename indicating the process to spawn, and optional argu-
ments to be passed to the process.
Options
spawn Programming Commands
“n” or “normal” Create process in normal mode. The process will typically
create a maximized window and may wait for user input.
“m” or
“minimized”
Create process in a minimized window. Note that some
applications may not accept requests to run in minimized
mode.
“h” or “hidden” Create process in hidden mode (without a visible window).
Note that some applications may not accept requests to run
in hidden mode.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-487-320.jpg)
![stats—481
Examples
spawn "c:program filesmicrosoft officeoffice11excel.exe"
test.xls
starts a new Excel process, passing it the command line argument “test.xls”.
Cross-references
See shell (p. 472) for information on starting a Windows command shell.
Descriptive statistics.
Computes and displays a table of means, medians, maximum and minimum values, stan-
dard deviations, and other descriptive statistics of one or more series or a group of series.
stats creates an untitled group containing all of the specified series, and opens a statistics
view of the group. By default, if more than one series is given, the statistics are calculated
for the common sample.
Syntax
stats(options) ser1 [ser2 ser3 …]
t=isecs Specifies the maximum time in seconds that EViews should
wait for the process to complete. If the timeout interval is
reached and the process has not completed, EViews will
generate an error. A timeout setting of zero may be used to
indicate that EViews should not wait for the spawned pro-
cess to complete. If no timeout option is provided, EViews
will wait indefinitely for the process to complete.
exit=icode Specifies the exit code that the process will return if it is
completed successfully. If the process returns an exit code
other than the specified value, EViews will generate an
error. If the exit code option is not specified, EViews will
not generate an error no matter what exit code is returned
by the process.
out =
tablename
If an output table name is specified, EViews will capture
any data written to standard output by the spawned pro-
cess and store it into a table object with the specified name
in the workfile. Note that this option will have no effect
unless either the minimized or hidden option is used and
the timeout value is not zero.
stats Interactive Use Commands](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-488-320.jpg)
![stepls—483
Estimation by stepwise least squares.
Syntax
stepls(options) y x1 [x2 x3 ...] @ z1 z2 z3
Specify the dependent variable followed by a list of variables to be included in the regres-
sion, but not part of the search routine, followed by an “@” symbol and a list of variables to
be part of the search routine. If no included variables are required, simply follow the depen-
dent variable with an “@” symbol and the list of search variables.
Options
stepls Interactive Use Commands
method = arg Stepwise regression method: “stepwise” (default), “uni”
(uni-directional), “swap” (swapwise), “comb” (combinato-
rial).
nvars = int Set the number of search regressors. Required for swapwise
and combinatorial methods, optional for uni-directional
and stepwise methods.
w=arg Weight series or expression.
Note: we recommend that, absent a good reason, you
employ the default settings Inverse std. dev. weights
(“wtype=istdev”) with EViews default scaling
(“wscale=eviews”) for backward compatibility with ver-
sions prior to EViews 7.
wtype=arg
(default=“istdev”)
Weight specification type: inverse standard deviation (“ist-
dev”), inverse variance (“ivar”), standard deviation
(“stdev”), variance (“var”).
wscale=arg Weight scaling: EViews default (“eviews”), average
(“avg”), none (“none”).
The default setting depends upon the weight type:
“eviews” if “wtype=istdev”, “avg” for all others.
coef=arg Specify the name of the coefficient vector (if specified by
list); the default behavior is to use the “C” coefficient vec-
tor.
prompt Force the dialog to appear from within a program.
p Print estimation results.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-490-320.jpg)
![store—485
Syntax
store(options) object_list
Follow the store command keyword with a list of object names (each separated by a space)
that you wish to store. The default is to store the objects in the default database. (This
behavior is a change from EViews 2 and earlier where the default was to store objects in indi-
vidual databank files).
You may precede the object name with a database name and the double colon “::” to indi-
cate a specific database. You can also specify the database name as an option in parenthe-
ses, in which case all objects without an explicit database name will be stored in the
specified database.
You may use wild card characters “?” (to match any single character) or “*” (to match zero
or more characters) in the object name list. All objects with names matching the pattern will
be stored.
You can optionally choose to store the listed objects in individual databank files. To store in
files other than the default path, you should include a path designation before the object
name.
Options
If you do not specify the precision option (1 or 2), the global option setting will be used. See
“Database Storage Defaults” on page 823 of the User’s Guide II.
Examples
store m1 gdp unemp
stores the three objects M1, GDP, UNEMP in the default database.
store(d=us1) m1 gdp macro::unemp
stores M1 and GDP in the US1 database and UNEMP in the MACRO database.
store usdat::gdp macro::gdp
d=db_name Store to the specified database.
i Store to individual databank files.
1 / 2 Store series in [single / double] precision to save space.
o Overwrite object in database (default is to merge data,
where possible).
g=arg Group store from workfile to database: “s” (copy group
definition and series as separate objects), “t” (copy group
definition and series as one object), “d” (copy series only
as separate objects), “l” (copy group definition only).](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-492-320.jpg)
![486—Chapter 12. Command Reference
stores the same object GDP in two different databases USDAT and MACRO.
store(1) cons*
stores all objects with names starting with CONS in the default database. The “1” option
uses single precision to save space.
store(i) m1 c:dataunemp
stores M1 and UNEMP in individual databank files.
Cross-references
“Basic Data Handling” on page 119 of the User’s Guide I discusses exporting data in other
file formats. See Chapter 10. “EViews Databases,” on page 307 of the User’s Guide I for a dis-
cussion of EViews databases and databank files.
For additional discussion of wildcards, see Appendix A. “Wildcards,” on page 739 of the
User’s Guide I.
See also fetch (p. 355) and copy (p. 327).
Estimate a switching regression model (simple exogenous or Markov).
Syntax
switchreg(options) dependent_var list_of_varying_regressors [ @nv list_of_nonvary-
ing_regressors ] [ @prv list_of_probability_regressors ]
List the switchreg keyword, followed by options, then the dependent variable and a list of
the regressors with regime-varying coefficients, following optionally by the keyword @nv
and a list of regressors with regime-invariant coefficients, and by the keyword @prv and a
list of regressors that enter into the transition probability specification.
The dependent variable in switchreg may not be an expression. Dynamics may be speci-
fied by including lags of the dependent variable as regressors, or by specifying AR errors
using the AR keyword. The latter incorporate mean adjusted lags of the form specified by the
“Hamilton-model.”
Options
switchreg Interactive Use Commands
type=arg Type of switching: simple exogenous (“simple”), Markov
(“markov”).
nstates=integer
(default=2)
Number of regimes.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-493-320.jpg)
![488—Chapter 12. Command Reference
Additional Options
Examples
switchreg(type=markov) y c @nv ar(1) ar(2) ar(3) ar(4)
optmethod = arg Optimization method: “bfgs” (BFGS); “newton” (Newton-
Raphson), “opg” or “bhhh” (OPG or BHHH), “legacy”
(EViews legacy).
BFGS is the default method.
optstep = arg Step method: “marquardt” (Marquardt); “dogleg” (Dog-
leg); “linesearch” (Line search).
Marquardt is the default method.
m=integer Set maximum number of iterations.
c=scalar Set convergence criterion. The criterion is based upon the
maximum of the percentage changes in the scaled coeffi-
cients. The criterion will be set to the nearest value
between 1e-24 and 0.2.
cov=arg Covariance method: “ordinary” (default method based on
inverse of the estimated information matrix), “huber” or
“white” (Huber-White sandwich method).
covinfo = arg Information matrix method: “opg” (OPG); “hessian”
(observed Hessian).
(Applicable when non-legacy “optmethod=”.)
nodf Do not degree-of-freedom correct the coefficient covariance
estimate.
coef=arg Specify the name of the coefficient vector (if specified by
list); the default behavior is to use the “C” coefficient vec-
tor.
s Use the current coefficient values in “C” as starting values
(see also param (p. 453) of the Command and Program-
ming Reference).
s=number Specify a number between zero and one to determine start-
ing values as a fraction of EViews default values (out of
range values are set to “s=1”).
showopts / -showopts [Do / do not] display the starting coefficient values and
estimation options in the estimation output.
prompt Force the dialog to appear from within a program.
p Print results.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-495-320.jpg)
![490—Chapter 12. Command Reference
name of the table to copy to, and dr1 and dr2 specify the upper and left most row and col-
umn of the destination table, respectively.
Examples
tabplace(table2,table1,"d1","B9","E17")
places a copy of the data from cell range B9 to E17 in TABLE1 to TABLE2 at cell D1
tabplace(table3,table1,10,3,9,2,17,5)
copies 8 rows of data (from row 9 to row 17) and 3 columns (from 2 to 5)of data in TABLE1
to the tenth row and 3rd column of TABLE3.
Cross-references
See also Table::copytable (p. 775).
For additional discussion of table commands see Chapter 3. “Working with Tables and
Spreadsheets,” on page 57 of the Command and Programming Reference.
See also Chapter 16. “Table and Text Objects,” on page 743 of the User’s Guide I for a discus-
sion and examples of table formatting in EViews.
Test whether to add regressors to an estimated equation.
Tests the hypothesis that the listed variables were incorrectly omitted from an estimated
equation (only available for equations estimated by list). The test displays some combina-
tion of Wald and LR test statistics, as well as the auxiliary regression.
Syntax
testadd(options) arg1 [arg2 arg3 ...]
List the names of the series or groups of series to test for omission after the keyword. The
test is applied to the default equation, if defined.
Options
Examples
ls sales c adver lsales ar(1)
testadd gdp gdp(-1)
tests whether GDP and GDP(-1) belong in the specification for SALES. The commands:
testadd Interactive Use Commands
prompt Force the dialog to appear from within a program.
p Print output from the test.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-497-320.jpg)
![threshold—491
Cross-references
See “Coefficient Diagnostics” on page 164 of the User’s Guide II for further discussion.
See also testdrop (p. 491).
Test whether to drop regressors from a regression.
Tests the hypothesis that the listed variables were incorrectly included in the estimated
equation (only available for equations estimated by list). The test displays some combina-
tion of and LR test statistics, as well as the test regression.
Syntax
testdrop(options) arg1 [arg2 arg3 ...]
List the names of the series or groups of series to test for omission after the keyword. The
test is applied to the default equation, if defined.
Options
Examples
ls sales c adver lsales ar(1)
testdrop adver
tests whether ADVER should be excluded from the specification for SALES. The commands:
Cross-references
See “Coefficient Diagnostics” on page 164 of the User’s Guide II for further discussion of
testing coefficients.
See also testadd (p. 490) and Equation::wald (p. 175) in the Object Reference.
Estimation by threshold least squares, including threshold autoregression.
Syntax
threshold(options) y z1 [z2 z3 ...] [@nv x1 x2 x3 ...] @thresh t1 [t2 t3 ...]
testdrop Interactive Use Commands
prompt Force the dialog to appear from within a program.
p Print output from the test.
threshold Interactive Use Commands
F](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-498-320.jpg)
![toc—495
[some commands]
toc
resets the timer, executes commands, and then displays the elapsed time in the status line.
Alternatively:
tic
[some commands]
!elapsed = @toc
resets the time, executes commands, and saves the elapsed time in the control variable
!ELAPSED.
Cross-references
See also toc (p. 495) and @toc (p. 726).
Display elapsed time (since timer reset) in seconds.
Syntax
Command: toc
Examples
The sequence of commands:
tic
[some commands]
toc
resets the timer, executes commands, and then displays the elapsed time in the status line.
The set of commands:
tic
[some commands]
!elapsed = @toc
[more commands]
toc
resets the time, executes commands, saves the elapsed time in the control variable
!ELAPSED, executes additional commands, and displays the total elapsed time in the status
line.
Cross-references
See also tic (p. 494) and @toc (p. 726).
toc Programming Commands](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-502-320.jpg)
![496—Chapter 12. Command Reference
Two-stage least squares.
Carries out estimation using two-stage least squares.
Syntax
tsls(options) y x1 [x2 x3 ...] @ z1 [z2 z3 ...]
tsls(options) specification @ z1 [z2 z3 ...]
To use the tsls command, list the dependent variable first, followed by the regressors, then
any AR or MA error specifications, then an “@”-sign, and finally, a list of exogenous instru-
ments. You may estimate nonlinear equations or equations specified with formulas by first
providing a specification, then listing the instrumental variables after an “@”-sign.
There must be at least as many instrumental variables as there are independent variables.
All exogenous variables included in the regressor list should also be included in the instru-
ment list. A constant is included in the list of instrumental variables even if not explicitly
specified.
Options
Non-Panel TSLS Options
tsls Interactive Use Commands
nocinst Do not automatically include a constant as an instrument.
w=arg Weight series or expression.
Note: we recommend that, absent a good reason, you
employ the default settings Inverse std. dev. weights
(“wtype=istdev”) with EViews default scaling
(“wscale=eviews”) for backward compatibility with ver-
sions prior to EViews 7.
wtype=arg
(default=“istdev”)
Weight specification type: inverse standard deviation (“ist-
dev”), inverse variance (“ivar”), standard deviation
(“stdev”), variance (“var”).
wscale=arg Weight scaling: EViews default (“eviews”), average
(“avg”), none (“none”).
The default setting depends upon the weight type:
“eviews” if “wtype=istdev”, “avg” for all others.
z Turn off backcasting in ARMA models.
cov=keyword Covariance type (optional): “white” (White diagonal
matrix), “hac” (Newey-West HAC).](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-503-320.jpg)
![498—Chapter 12. Command Reference
Panel TSLS Options
s=number Determine starting values for equations specified by list
with AR or MA terms. Specify a number between zero and
one representing the fraction of TSLS estimates computed
without AR or MA terms to be used. Note that out of range
values are set to “s=1”. Specifying “s=0” initializes coeffi-
cients to zero. By default EViews uses “s=1”.
Does not apply to coefficients for AR and MA terms which
are set to EViews determined default values.
numericderiv /
-numericderiv
[Do / do not] use numeric derivatives only. If omitted,
EViews will follow the global default.
fastderiv / -fastderiv [Do / do not] use fast derivative computation. If omitted,
EViews will follow the global default.
showopts /
-showopts
[Do / do not] display the starting coefficient values and
estimation options in the estimation output.
coef=arg Specify the name of the coefficient vector (if specified by
list); the default behavior is to use the “C” coefficient vec-
tor.
prompt Force the dialog to appear from within a program.
p Print basic estimation results.
cx=arg Cross-section effects. For fixed effects estimation, use
“cx=f”; for random effects estimation, use “cx=r”.
per=arg Period effects. For fixed effects estimation, use “cx=f”; for
random effects estimation, use “cx=r”.
wgt=arg GLS weighting: (default) none, cross-section system
weights (“wgt=cxsur”), period system weights
(“wgt=persur”), cross-section diagonal weighs
(“wgt=cxdiag”), period diagonal weights (“wgt=per-
diag”).
cov=arg Coefficient covariance method: (default) ordinary, White
cross-section system robust (“cov=cxwhite”), White
period system robust (“cov=perwhite”), White heteroske-
dasticity robust (“cov=stackedwhite”), Cross-section sys-
tem robust/PCSE (“cov=cxsur”), Period system
robust/PCSE (“cov=persur”), Cross-section heteroskedas-
ticity robust/PCSE (“cov=cxdiag”), Period heteroskedastic-
ity robust (“cov=perdiag”).
keepwgts Keep full set of GLS weights used in estimation with object,
if applicable (by default, only small memory weights are
saved).](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-505-320.jpg)
![tsls—499
rancalc=arg
(default=“sa”)
Random component method: Swamy-Arora (“ran-
calc=sa”), Wansbeek-Kapteyn (“rancalc=wk”), Wallace-
Hussain (“rancalc=wh”).
nodf Do not perform degree of freedom corrections in computing
coefficient covariance matrix. The default is to use degree
of freedom corrections.
iter=arg
(default=“onec”)
Iteration control for GLS specifications: perform one weight
iteration, then iterate coefficients to convergence
(“iter=onec”), iterate weights and coefficients simultane-
ously to convergence (“iter=sim”), iterate weights and
coefficients sequentially to convergence (“iter=seq”), per-
form one weight iteration, then one coefficient step
(“iter=oneb”).
Note that random effects models currently do not permit
weight iteration to convergence.
unbalsur Compute SUR factorization in unbalanced data using the
subset of available observations for a cluster.
coef=arg Specify the name of the coefficient vector (if specified by
list); the default is to use the “C” coefficient vector.
s Use the current coefficient values in estimator coefficient
vector as starting values for equations specified by list with
AR terms (see also param (p. 453) of the Command and
Programming Reference).
s=number Determine starting values for equations specified by list
with AR terms. Specify a number between zero and one
representing the fraction of TSLS estimates computed with-
out AR terms to be used. Note that out of range values are
set to “s=1”. Specifying “s=0” initializes coefficients to
zero. By default EViews uses “s=1”.
Does not apply to coefficients for AR terms which are
instead set to EViews determined default values.
m=integer Set maximum number of iterations.
c=number Set convergence criterion. The criterion is based upon the
maximum of the percentage changes in the scaled coeffi-
cients. The criterion will be set to the nearest value
between 1e-24 and 0.2.
numericderiv /
-numericderiv
[Do / do not] use numeric derivatives only. If omitted,
EViews will follow the global default.
fastderiv / -fastderiv [Do / do not] use fast derivative computation. If omitted,
EViews will follow the global default.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-506-320.jpg)
![500—Chapter 12. Command Reference
Examples
tsls y_d c cpi inc ar(1) @ lw(-1 to -3)
estimates an UNTITLED equation using TSLS regression of Y_D on a constant, CPI, INC with
AR(1) using a constant, LW(-1), LW(-2), and LW(-3) as instruments.
param c(1) .1 c(2) .1
tsls(s,m=500) y_d=c(1)+inc^c(2) @ cpi
estimates a nonlinear TSLS model using a constant and CPI as instruments. The first line
sets the starting values for the nonlinear iteration algorithm.
Cross-references
See Chapter 20. “Additional Regression Tools,” on page 23 and “Two-Stage Least Squares”
on page 603 of the User’s Guide II for details on two-stage least squares estimation in single
equations and systems, respectively. “Instrumental Variables” on page 824 of the User’s
Guide II discusses estimation using pool objects, while “Instrumental Variables Estimation”
on page 858 of the User’s Guide II discusses estimation in panel structured workfiles.
See also ls (p. 410).
Andrews-Quandt test for unknown breakpoint.
Carries out the Andrews-Quandt test for parameter stability at some unknown breakpoint.
Syntax
ubreak(options) trimlevel @ x1 x2 x3
You must provide the level of trimming of the data. The level must be one of the following:
49, 48, 47, 45, 40, 35, 30, 25, 20, 15, 10, or 5. If the equation is specified by list and contains
no linear terms, you may specify a subset of the regressors to be tested for a breakpoint after
an “@” sign.
showopts /
-showopts
[Do / do not] display the starting coefficient values and
estimation options in the estimation output.
prompt Force the dialog to appear from within a program.
p Print estimation results.
ubreak Interactive Use Commands](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-507-320.jpg)
![506—Chapter 12. Command Reference
uroot(kpss,const,hac=pr,b=2.3) unemp
runs the KPSS test on the series UNEMP. The null hypothesis is that the series is stationary
around a constant mean. The frequency zero spectrum is estimated using kernel methods
(with a Parzen kernel), and a bandwidth of 2.3.
uroot(np,hac=ardt,infosel=maic) sp500
runs the NP test on the series SP500. The frequency zero spectrum is estimated using the
OLS AR spectral estimator with the lag length automatically selected using the modified
AIC.
Cross-references
See “Unit Root Testing” on page 545 of the User’s Guide II for discussion of standard unit
root tests performed on a single series, and “Panel Unit Root Testing” on page 573 of the
User’s Guide II for discussion of unit roots tests performed on panel structured workfiles,
groups of series, or pooled data.
Specify and estimate a VAR or VEC.
Syntax
varest(options) lag_pairs endog_list [@ exog_list]
varest(method=ec, trend, n, options) lag_pairs endog_list [@ exog_list]
The first form of the command estimates a VAR. It is the interactive command equivalent of
using ls to estimate a named VAR object (see Var::ls (p. 852) in the Object Reference for
syntax and details).
The second form of the command estimates a VEC. It is the interactive command equivalent
of using ec to estimate a named VAR object (see Var::ec (p. 842) in the Object Reference
for syntax and details).
Examples
Examples
varest 1 3 m1 gdp
estimates an unnamed unrestricted VAR with two endogenous variables (M1 and GDP), a
constant and 3 lags (lags 1 through 3).
varest Interactive Use Commands
prompt Force the dialog to appear from within a program.
p Print output from the test.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-513-320.jpg)
![wfclose—507
varest(noconst) 1 3 ml gdp
estimates the same VAR, but with no constant term included in the specification.
varest(method=ec) 1 4 m1 gdp tb3
estimates a VEC with four lagged first differences, three endogenous variables and one
cointegrating equation using the default trend option “c”.
varest(method=ec,b,2) 1 2 4 4 tb1 tb3 tb6 @ d2 d3 d4
estimates a VEC with lagged first differences of order 1, 2, 4, three endogenous variables,
three exogenous variables, and two cointegrating equations using trend option “b”.
Cross-references
See also Var::var (p. 864), Var::ls (p. 852) and Var::ec (p. 842) in the Object Refer-
ence.
Close the active or specified workfile.
Syntax
wfclose(options) [name]
wfclose allows you to close the currently active workfile. You may optionally provide the
name of a workfile if you do not wish to close the active workfile. If more than one workfile
is found with the same name, the one most recently opened will be closed.
Options
Examples
wfclose
closes the workfile that was most recently active.
wfclose basics
closes the workfile named BASICS.
Cross-references
See also wfcreate (p. 510), wfopen (p. 515) and wfsave (p. 529).
wfclose Object Container, Data, and File Commands
noerr Do not error if a name is provided and no workfile with
that name is found.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-514-320.jpg)
![508—Chapter 12. Command Reference
Compare the contents of the current workfile or page with the contents of a different work-
file, page or database.
Syntax
wfcompare(options) targetspec baselinespec [@keep keeplist @drop droplist]
The command shows a list of any differences between the objects specified by targetspec
and those specified by baselinespec.
targetspec should be a specification of objects in the current workfile. It should take the form
of [page]name_pattern, where the optional page may be used to specify a specific page of
the current workfile. name_pattern is used to list the objects you wish to compare. The typi-
cal form of targetspec is simply “*”, meaning to compare all objects in the current workfile.
baselinespec should be a specification of the list of objects to compare against. It should take
the form of [container::page]name_pattern, where the optional container::page may be
used to specify the name of the workfile or database or page containing the objects to com-
pare against. name_pattern is used to list the objects you wish to compare against. If base-
linespec is blank, the version of the current workfile stored on disk is used as the baseline.
The optional @keep and @drop lists allow you to narrow further the list of objects in tar-
getspec by listing specific objects to compare (using @keep), or drop from the targetspec
(using @drop).
Options
wfcompare Object Container, Data, and File Commands
tol=arg Specifies the threshold below which differences between
the target and baseline values should be ignored. The
threshold is specified as a fraction of the baseline value. For
example, if tol=1%, one or more observations in the target
object must differ from their values in the baseline object
by at least one percent for the objects to be reported as dif-
ferent. The default tolerance is 1e-15.
seterr Set an error if any differences exceeding the specified toler-
ance were found. This option may be useful in batch pro-
gramming to alert the user if a program causes
unexpectedly large changes to data values.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-515-320.jpg)
![510—Chapter 12. Command Reference
Cross-references
See “Comparing Workfiles,” on page 88 of User’s Guide I for discussion.
Create a new workfile. The workfile becomes the active workfile.
Syntax
wfcreate(options) frequency[(subperiod_opts)] start_date end_date [num_cross_sec-
tions]
wfcreate(options) u num_observations
The first form of the command may be used to create a new regular frequency workfile with
the specified frequency, start, and end date. Subperiod options may also be specified for
intraweek or intraday data. See table below for a complete description for each frequency. If
you include the optional num_cross_sections, EViews will create a balanced panel page
using integer identifiers for each of the cross-sections. Note that more complex panel struc-
tures may be created using pagestruct (p. 448). You may use the @now keyword to specify
the current date/time as either the start_date or end_date.
The second form of the command is used to create an unstructured workfile with the speci-
fied number of observations.
Options
Arguments
The frequency argument should be specified using one of the following forms:
wfcreate Object Container, Data, and File Commands
wf=wf_name Optional name for the new workfile.
page=page_name Optional name for the page in the new workfile.
prompt Force the dialog to appear from within a program.
Sec[opt], 5Sec[opt],
15Sec[opt],
30Sec[opt]
Seconds in intervals of: 1, 5, 15, or 30 seconds, respec-
tively. You may optionally specify days of the week and
start and end times during the day using the opt parameter.
See explanation of subperiod options below.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-517-320.jpg)
![wfcreate—511
Subperiod options
EViews allows for setting the days of the week and the time of day within intraday frequen-
cies, which include seconds, minutes, and hours. For instance, you may specify hourly data
between 8AM and 5PM on Monday through Wednesday. These subperiod options should
follow the frequency keyword and be enclosed in parentheses.
To specify days of the week, use integers to indicate the days, where Monday is given the
number 1 and Sunday is given the number 7. For example,
wfcreate(wf=storehours) 30MIN(1-6, 8:00-17:00) 1/3/2000 12/30/2000
Min[opt],
2Min[opt],
5Min[opt],
10Min[opt],
15Min[opt],
20Min[opt],
30Min[[opt]
Minutes in intervals of: 1, 2, 5, 10, 15, 20, or 30 minutes,
respectively. You may optionally specify days of the week
and start and end times during the day using the opt
parameter. See explanation of subperiod options below.
H[opt], 2H[opt],
4H[opt], 6H[opt],
8H[opt], 12H[[opt]
Hours in intervals of: 1, 2, 4, 6, 8, or 12 hours, respectively.
You may optionally specify days of the week and start and
end times during the day using the opt parameter. See
explanation of subperiod options below.
D(s, e) Daily with arbitrary days of the week. Specify the first and
last days of the week with integers s and e, where Monday
is given the number 1 and Sunday is given the number 7.
(Note that the “D” option used to specify a 5-day frequency
prior to EViews 7.)
D5 or 5 Daily with five days per week, Monday through Friday.
D7 or 7 Daily with seven days per week.
W Weekly
T Ten-day (daily in intervals of ten).
F Fortnight
BM Bimonthly
M Monthly
Q Quarterly
S Semi-annual
A or Y Annual
2Y, 3Y, 4Y, 5Y, 6Y,
7Y, 8Y, 9Y, 10Y, 20Y
Multi-year in intervals of: 2, 3, 4, 5, 6, 7, 8, 9, 10, or 20
years, respectively.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-518-320.jpg)
![514—Chapter 12. Command Reference
Change the details displayed in the current workfile window, optionally freezing the
results into a table object in the current workfile.
Syntax
wfdetails(options) col1[(width1)] [col2[(width2)].....] @sort sortcol
Specify the names of the attribute columns you would like to display in the workfile details
view, optionally including the width of the column in parenthesis. Widths can be specified
with positive numbers, indicating a width in pixels, or negative numbers if you wish to spec-
ify the width in approximate characters.
You may use the @sort keyword at the end of the specification to indicate by which column
to sort the details view.
Options
Examples
wfdetails start end
shows the workfile details view, with the attribute columns “start” and “end”.
wfdetails(out=dettable) start(30) end(-10)
shows the same view, but setting the “start” column’s width to 30 pixels, and the “end” col-
umn’s width to 10 characters, and freezing the view into a table called DETTABLE.
wfdetails start end @sortcol type
sorts the details view by object type.
Cross-references
See “Workfile Details Display,” on page 62 of User’s Guide I for discussion.
wfdetails Object Container, Data, and File Commands
out=name Create a table object name, containing the details view
table.
nohead Used with “out=” option to suppress the header row at the
top of the frozen table.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-521-320.jpg)
![wfopen—515
Change the workfile view to a simple object directory listing.
Syntax
wfdir
If the workfile is currently in details view, the wfdir command switches the view back to a
simple directory listing.
Cross-references
See “Workfile Details Display,” on page 62 of User’s Guide I for discussion.
Open a workfile. Reads in a previously saved workfile from disk, or reads the contents of a
foreign data source into a new workfile.
The opened workfile becomes the default workfile; existing workfiles in memory remain on
the desktop but become inactive.
Syntax
wfopen [path]source_name
wfopen(options) source_description [table_description] [variables_description]
wfopen(options) source_description [table_description] [dataset_modifiers]
where path is an optional local path or URL.
There are three basic forms of the wfopen command:
• the first form is used by EViews native (“EViews and MicroTSP” on page 518) and
time series database formats (“Time Series Formats” on page 518).
• the second form used for raw data files—Excel, Lotus, ASCII text, and binary files
(“Raw Data Formats” on page 519).
• the third form is used with the remaining source formats, which we term dataset for-
mats, since the data have already been arranged in named variables (“Datasets” on
page 526).
(See “Options” on page 517 for a description of the supported source formats and corre-
sponding types.)
wfdir Object Container, Data, and File Commands
wfopen Object Container, Data, and File Commands](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-522-320.jpg)
![516—Chapter 12. Command Reference
In all three cases, the workfile or external data source should be specified as the first argu-
ment following the command keyword and options.
• In most cases, the external data source is a file, so the source_description will be the
description of the file (including local path or URL information, if necessary). Alterna-
tively, the external data source may be the output from a web server, in which case
the URL should be provided. Similarly, when reading from an ODBC query, the ODBC
DSN (data source name) should be used as the source_description.
If the source_description contains spaces, it must be enclosed in (double) quotes.
For raw and dataset formats, you may use table_description to provide additional informa-
tion about the data to be read:
• Where there is more than one table that could be formed from the specified external
data source, a table_description may be provided to select the desired table. For exam-
ple, when reading from an Excel file, an optional cell range may be provided to specify
which data are to be read from the spreadsheet. When reading from an ODBC data
source, a SQL query or table name must be used to specify the table of data to be
read.
• In raw data formats, the table_description allows you to provide additional informa-
tion regarding names and descriptions for variables to be read, missing values codes,
settings for automatic format, and data range subsetting.
• When working with text or binary files, the table_description must be used to describe
how to break up the file into columns and rows.
For raw and dataset formats, you may use the dataset_modifiers specification to select the
set of variables, maps (value labels), and observations to be read from the source data. The
dataset_modifiers consists of the following keyword delimited lists:
[@keep keep_list] [@drop drop_list] [@keepmap keepmap_list] [@dropmap drop-
map_list] [@selectif condition]
• The @keep and @drop keywords, followed by a list of names and patterns, are used to
specify variables to be retain or dropped. Similarly, the @keepmap and @dropmap
keywords followed by lists of name patterns controls the reading of value labels. The
keyword @selectif, followed by an if condition (e.g., “if age>30 and gender=1”)
may be used to select a subset of the observations in the original data. By default, all
variables, value labels, and observations are read.
By default, all variables, maps and observations in the source file will be read.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-523-320.jpg)
![518—Chapter 12. Command Reference
EViews and MicroTSP
The syntax for EViews and MicroTSP files is:
wfopen [path]workfile_name
where path is an option local path or URL.
Examples
wfopen c:datamacro
loads a previously saved EViews workfile “Macro.WF1” from the “data” directory in the C
drive.
wfopen c:tspnipa.wf
loads a MicroTSP workfile “Nipa.WF”. If you do not use the workfile type option, you
should add the extension “.WF” to the workfile name when loading a DOS MicroTSP work-
file. An alternative method specifies the type explicitly:
wfopen(type=dos) nipa
The command:
wfopen “<mydropboxdrive>”foldernipa.wf1”
will open the file from the cloud location MYDROPBOXDRIVE.
Time Series Formats
The syntax for time series format files (Aremos-TSD, GiveWin/PcGive, RATS, RATS Porta-
ble/TROLL, TSP Portable) is:
wfopen(options) [path]source_name
where path is an optional local path or URL.
If the source files contain data of multiple frequencies, the resulting workfile will be of the
lowest frequency, and higher frequency data will be converted to this frequency. If you wish
to obtain greater control over the workfile creation, import, or frequency conversion pro-
cesses, we recommend that you open the file using dbopen (p. 344) and use the database
tools to create your workfile.
SAS Transport dataset “sasxport”
SPSS dataset “spss”
SPSS Portable dataset “spssport”
Stata dataset “stata”
Text / ASCII raw data “text”
TSP Portable time series database “t”, “tsp”](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-525-320.jpg)
![wfopen—519
Aremos Example
wfopen dlcs.tsd
wfopen(type=aremos) dlcs.tsd
open the AREMOS-TSD file DLCS.
GiveWin/PcGive Example
wfopen "f:projectpc givedatamacrodata.in7"
wfopen(type=give) "f:projectpc givedatamacrodata"
open the PcGive file MACRODATA.
Rats Examples
wfopen macrodata.rat"
wfopen macrodata.trl
read the native RATS 4.x file MACRODATA.RAT and the RATS Portable/TROLL file
“Macrodata.TRL”.
TSP Portable Example
wfopen macrodata.tsp
reads the TSP portable file “Macrodata.TSP”.
Raw Data Formats
The command for reading raw data (Excel 97-2003, Excel 2007, HTML, ASCII text, Binary,
Lotus 1-2-3) is
wfopen(options) source_description [table_description] [variables_description]
[@keep keep_list] [@drop drop_list] [@keepmap keepmap_list] [@dropmap
dropmap_list] [@selectif condition]
where the syntax of the table_description and variables_description differs slightly depend-
ing on the type of file.
Excel and Lotus Files
The syntax for reading Excel and Lotus files is:
wfopen(options) source_description [table_description] [variables_description]
The following table_description elements may be used when reading Excel and Lotus data:
• “range = arg”, where arg is a range of cells to read from the Excel workbook, follow-
ing the standard Excel format [worksheet!][topleft_cell[:bottomright_cell]].
If the worksheet name contains spaces, it should be placed in single quotes. If the
worksheet name is omitted, the cell range is assumed to refer to the currently active
sheet. If only a top left cell is provided, a bottom right cell will be chosen automati-](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-526-320.jpg)
![520—Chapter 12. Command Reference
cally to cover the range of non-empty cells adjacent to the specified top left cell. If
only a sheet name is provided, the first set of non-empty cells in the top left corner of
the chosen worksheet will be selected automatically. As an alternative to specifying
an explicit range, a name which has been defined inside the excel workbook to refer
to a range or cell may be used to specify the cells to read.
• “byrow”, transpose the incoming data. This option allows you to read files where the
series are contained in rows (one row per series) rather than columns.
The optional variables_description may be formed using the elements:
• “colhead=int”, number of table rows to be treated as column headers.
• “namepos = [first|last|all|none]”, which row(s) of the column headers should be
used to form the column name. The setting “first” refers to first line, “last” is last line,
“all” is all lines and “none” is no lines. The remaining column header rows will be
used to form the column description. The default setting is “all”.
• “Nonames”, the file does not contain a column header (same as “colhead=0”).
• “names=("arg1","arg2",…)”, user specified column names, where arg1, arg2, … are
names of the first series, the second series, etc. when names are provided, these over-
ride any names that would otherwise be formed from the column headers.
• “descriptions=("arg1","arg2",…)”, user specified descriptions of the series. If
descriptions are provided, these override any descriptions that would otherwise be
read from the data.
• “types=("arg1","arg2",…)”, user specified data types of the series. If types are pro-
vided they will override the types automatically detected by EViews. You may use any
of the following format keywords: “a” (character data), “f” (numeric data), “d”
(dates), or “w”(EViews automatic detection).
• “na="arg1"”, text used to represent observations that are missing from the file. The
text should be enclosed on double quotes.
• “scan=[int| all]”, number of rows of the table to scan during automatic format detec-
tion (“scan=all” scans the entire file).
• “firstobs=int”, first observation to be imported from the table of data (default is 1).
This option may be used to start reading rows from partway through the table.
• “lastobs = int”, last observation to be read from the table of data (default is last
observation of the file). This option may be used to read only part of the file, which
may be useful for testing.
Excel Examples
wfopen "c:data filesdata.xls"](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-527-320.jpg)
![wfopen—521
loads the active sheet of DATA.XLS into a new workfile.
wfopen(page=mypage) "c:data filesdata.xls" range="GDP data"
@drop X
reads the data contained in the “GDP data” sheet of “Data.XLS” into the MYPAGE page of a
new workfile. The data for the series X is dropped, and the name of the new workfile page is
“GDP”.
To load the Excel file containing US Macro Quarterly data from Stock and Watson’s Introduc-
tion to Econometrics you may use the command:
wfopen
http//wps.aw.com/wps/media/objects/3254/3332253/datasets2e/dat
asets/USMacro_Quarterly.xls
which will load the Excel file directly into EViews from the publisher’s website (as of
08/2009).
HTML Files
The syntax for reading HTML pages is:
wfopen(options) source_description [table_description] [variables_description]
The following table_description elements may be used when reading an HTML file or page:
• “table = arg”, where arg specifies which table to read in an HTML file/page contain-
ing multiple tables.
When specifying arg, you should remember that tables are named automatically fol-
lowing the pattern “Table01”, “Table02”, “Table03”, etc. If no table name is specified,
the largest table found in the file will be chosen by default. Note that the table num-
bering may include trivial tables that are part of the HTML content of the file, but
would not normally be considered as data tables by a person viewing the page.
• “skip = int”, where int is the number of rows to discard from the top of the HTML
table.
• “byrow”, transpose the incoming data. This option allows you to import files where
the series are contained in rows (one row per series) rather than columns.
The optional variables_description may be formed using the elements:
• “colhead=int”, number of table rows to be treated as column headers.
• “namepos = [first|last|all|none]”, which row(s) of the column headers should be
used to form the column name. The setting “first” refers to first line, “last” is last line,
“all” is all lines and “none” is no lines. The remaining column header rows will be
used to form the column description. The default setting is “all”.
• “nonames”, the file does not contain a column header (same as “colhead=0”).](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-528-320.jpg)
![522—Chapter 12. Command Reference
• “names=("arg1","arg2",…)”, user specified column names, where arg1, arg2, … are
names of the first series, the second series, etc. when names are provided, these over-
ride any names that would otherwise be formed from the column headers.
• “descriptions=("arg1","arg2",…)”, user specified descriptions of the series. If
descriptions are provided, these override any descriptions that would otherwise be
read from the data.
• “types=("arg1","arg2",…)”, user specified data types of the series. If types are pro-
vided they will override the types automatically detected by EViews. You may use any
of the following format keywords: “a” (character data), “f” (numeric data), “d”
(dates), or “w”(EViews automatic detection).
• “na="arg1"”, text used to represent observations that are missing from the file. The
text should be enclosed on double quotes.
• “scan=[int|all]”, number of rows of the table to scan during automatic format detec-
tion (“scan=all” scans the entire file).
• “firstobs=int”, first observation to be imported from the table of data (default is 1).
This option may be used to start reading rows from partway through the table.
• “lastobs = int”, last observation to be read from the table of data (default is last
observation of the file). This option may be used to read only part of the file, which
may be useful for testing.
HTML Examples
wfopen "c:data.html"
loads into a new workfile the data located on the HTML file “Data.HTML” located on the C:
drive
wfopen(type=html)
"http://www.tradingroom.com.au/apps/mkt/forex.ac" colhead=3,
namepos=first
loads into a new workfile the data with the given URL located on the website site
“http://www.tradingroom.com.au”. The column header is set to three rows, with the first
row used as names for columns, and the remaining two lines used to form the descriptions.
Text and Binary Files
The syntax for reading text or binary files is:
wfopen(options) source_description [table_description] [variables_description]
If a table_description is not provided, EViews will attempt to read the file as a free-format
text file. The following table_description elements may be used when reading a text or
binary file:](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-529-320.jpg)
![wfopen—523
• “ftype = [ascii|binary]” specifies whether numbers and dates in the file are stored in
a human readable text (ASCII), or machine readable (Binary) form.
• “rectype = [crlf|fixed|streamed]” describes the record structure of the file:
“crlf”, each row in the output table is formed using a fixed number of lines from
the file (where lines are separated by carriage return/line feed sequences). This is
the default setting.
“fixed”, each row in the output table is formed using a fixed number of charac-
ters from the file (specified in “reclen= arg”). This setting is typically used for
files that contain no line breaks.
“streamed”, each row in the output table is formed by reading a fixed number of
fields, skipping across lines if necessary. This option is typically used for files that
contain line breaks, but where the line breaks are not relevant to how rows from
the data should be formed.
• “reclines =int”, number of lines to use in forming each row when “rectype=crlf”
(default is 1).
• “reclen=int”, number of bytes to use in forming each row when “rectype=fixed”.
• “recfields=int”, number of fields to use in forming each row when “rec-
type=streamed”.
• “skip=int”, number of lines (if rectype is “crlf”) or bytes (if rectype is not “crlf”) to
discard from the top of the file.
• “comment=string“, where string is a double-quoted string, specifies one or more
characters to treat as a comment indicator. When a comment indicator is found,
everything on the line to the right of where the comment indicator starts is ignored.
• “emptylines=[keep|drop]”, specifies whether empty lines should be ignored
(“drop”), or treated as valid lines (“keep”) containing missing values. The default is
to ignore empty lines.
• “tabwidth=int”, specifies the number of characters between tab stops when tabs are
being replaced by spaces (default=8). Note that tabs are automatically replaced by
spaces whenever they are not being treated as a field delimiter.
• “fieldtype=[delim|fixed|streamed|undivided]”, specifies the structure of fields within
a record:
“Delim”, fields are separated by one or more delimiter characters
“Fixed”, each field is a fixed number of characters
“Streamed”, fields are read from left to right, with each field starting immediately
after the previous field ends.
“Undivided”, read entire record as a single series.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-530-320.jpg)
![524—Chapter 12. Command Reference
• “quotes=[single|double|both|none]”, specifies the character used for quoting fields,
where “single” is the apostrophe, “double” is the double quote character, and “both”
means that either single or double quotes are allowed (default is “both”). Characters
contained within quotes are never treated as delimiters.
• “singlequote“, same as “quotes = single”.
• “delim=[comma|tab|space|dblspace|white|dblwhite]”, specifies the character(s) to
treat as a delimiter. “White” means that either a tab or a space is a valid delimiter. You
may also use the abbreviation “d=” in place of “delim=”.
• “custom="arg1"”, specifies custom delimiter characters in the double quoted string.
Use the character “t” for tab, “s” for space and “a” for any character.
• “mult=[on|off]”, to treat multiple delimiters as one. Default value is “on” if “delim”
is “space”, “dblspace”, “white”, or “dblwhite”, and “off” otherwise.
• “endian = [big|little]”, selects the endianness of numeric fields contained in binary
files.
• “string = [nullterm|nullpad|spacepad]”, specifies how strings are stored in binary
files. If “nullterm”, strings shorter than the field width are terminated with a single
zero character. If “nullpad”, strings shorter than the field width are followed by extra
zero characters up to the field width. If “spacepad”, strings shorter than the field
width are followed by extra space characters up to the field width.
• “byrow”, transpose the incoming data. This option allows you to import files where
the series are contained in rows (one row per series) rather than columns.
A central component of the table_description element is the format statement. You may
specify the data format using the following table descriptors:
• Fortran Format:
fformat=([n1]Type[Width][.Precision], [n2]Type[Width][.Precision], ...)
where Type specifies the underlying data type, and may be one of the following,
I - integer
F - fixed precision
E - scientific
A - alphanumeric
X - skip
and n1, n2, ... are the number of times to read using the descriptor (default=1). More
complicated Fortran compatible variations on this format are possible.
• Column Range Format:
rformat="[n1]Type[Width][.Precision], [n2]Type[Width][.Precision], ...)"](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-531-320.jpg)
![wfopen—525
where optional type is “$” for string or “#” for number, and n1, n2, n3, n4, etc. are the
range of columns containing the data.
• C printf/scanf Format:
cformat="fmt"
where fmt follows standard C language (printf/scanf) format rules.
The optional variables_description may be formed using the elements:
• “colhead=int”, number of table rows to be treated as column headers.
• “namepos = [first|last|all|none]”, which row(s) of the column headers should be
used to form the column name. The setting “first” refers to first line, “last” is last line,
“all” is all lines and “none” is no lines. The remaining column header rows will be
used to form the column description. The default setting is “all”.
• “nonames”, the file does not contain a column header (same as “colhead=0”).
• “names=("arg1", "arg2",…)”, user specified column names, where arg1, arg2, … are
names of the first series, the second series, etc. when names are provided, these over-
ride any names that would otherwise be formed from the column headers.
• “descriptions=("arg1", "arg2",…)”, user specified descriptions of the series. If
descriptions are provided, these override any descriptions that would otherwise be
read from the data.
• “types=("arg1","arg2",…)”, user specified data types of the series. If types are pro-
vided they will override the types automatically detected by EViews. You may use any
of the following format keywords: “a” (character data), “f” (numeric data), “d”
(dates), or “w”(EViews automatic detection).
• “na="arg1"”, text used to represent observations that are missing from the file. The
text should be enclosed on double quotes.
• “scan=[int|all]”, number of rows of the table to scan during automatic format detec-
tion (“scan=all” scans the entire file).
• “firstobs=int”, first observation to be imported from the table of data (default is 1).
This option may be used to start reading rows from partway through the table.
• “lastobs = int”, last observation to be read from the table of data (default is last
observation of the file). This option may be used to read only part of the file, which
may be useful for testing.
Text and Binary File Examples (.txt, .csv, etc.)
wfopen c:data.csv skip=5, names=(gdp, inv, cons)
reads “Data.CSV” into a new workfile page, skipping the first 5 rows and naming the series
GDP, INV, and CONS.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-532-320.jpg)
![526—Chapter 12. Command Reference
wfopen(type=text) c:date.txt delim=comma
loads the comma delimited data DATE.TXT into a new workfile.
wfopen(type=raw) c:data.txt skip=8, rectype=fixed,
format=(F10,X23,A4)
loads a text file with fixed length data into a new workfile, skipping the first 8 rows. The
reading is done as follows: read the first 10 characters as a fixed precision number, after that,
skip the next 23 characters (X23), and then read the next 4 characters as strings (A4).
wfopen(type=raw) c:data.txt rectype=fixed, format=2(4F8,2I2)
loads the text file as a workfile using the specified explicit format. The data will be a repeat
of four fixed precision numbers of length 8 and two integers of length 2. This is the same
description as “format=(F8,F8,F8,F8,I2,I2,F8,F8,F8,F8,I2,I2)”.
wfopen(type=raw) c:data.txt rectype=fixed, rformat="GDP 1-2 INV 3
CONS 6-9"
loads the text file as a workfile using column range syntax. The reading is done as follows:
the first series is located at the first and second character of each row, the second series
occupies the 3rd character, the third series is located at character 6 through 9. The series will
named GDP, INV, and CONS.
Datasets
The syntax for reading data from the remaining sources (Access, Gauss, ODBC, SAS pro-
gram, SAS transport, SPSS, SPSS portable, Stata) is:
wfopen(options) source_description table_description [@keep keep_list] [@drop
drop_list] [@selectif condition]
Note that for this purpose we view Access and ODBC as datasets.
ODBC or Microsoft Access
The syntax for reading from an ODBC or Microsoft Access data source is
wfopen(options) source_description table_description [@keep keep_list] [@drop
drop_list] [@selectif condition]
When reading from an ODBC or Microsoft Access data source, you must provide a table_de-
scription to indicate the table of data to be read. You may provide this information in one of
two ways: by entering the name of a table in the data source, or by including an SQL query
statement enclosed in double quotes.
Note that ODBC support is provided only in the EViews Enterprise Edition.
ODBC Examples
wfopen c:data.dsn CustomerTable](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-533-320.jpg)
![wfopen—527
opens in a new workfile the table named CUSTOMERTABLE from the ODBC database
described in the DATA.DSN file.
wfopen(type=odbc) "my server" "select * from customers where id>30"
@keep p*
opens in a new workfile with SQL query from database using the server “MY SERVER”,
keeping only variables that begin with P. The query selects all variables from the table CUS-
TOMERS where the ID variable takes a value greater than 30.
Other Dataset Types
The syntax for reading data from the remaining sources (Gauss, SAS program, SAS trans-
port, SPSS, SPSS portable, Stata) is:
wfopen(options) source_description [@keep keep_list] [@drop drop_list] [@selectif
condition]
Note that no table_description is required.
SAS Program Example
If a data file, “Sales.DAT”, contains the following space delimited data:
AZ 110 1002
CA 200 2003
NM 90 908
OR 120 708
WA 113 1123
UT 98 987
then the following SAS program file can be read by EViews to open the data:
Data sales;
infile sales.dat';
input state $ price sales;
run;
SAS Transport Examples
wfopen(type=sasxport) c:data.xpt
loads a SAS transport file “data.XPT” into a new workfile.
wfopen c:inst.sas
creates a workfile by reading from external data using the SAS program statements in
“Inst.SAS”. The program may contain a limited set of SAS statements which are commonly
used in reading in a data file.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-534-320.jpg)
![wfsave—529
Save the default workfile as an EViews workfile (.wf1 file) or as a foreign file or data
source.
Syntax
wfsave(options) [path]filename
wfsave(options) source_description [@keep keep_list] [@drop drop_list] [@keepmap
keepmap_list] [@dropmap dropmap_list] [@smpl smpl_spec]
wfsave(options) source_description table_description [@keep keep_list] [@drop
drop_list] [@keepmap keepmap_list] [@dropmap dropmap_list] [@smpl
smpl_spec]
saves the active workfile in the specified directory using filename. By default, the workfile is
saved as an EViews workfile, but options may be used to save all or part of the active page in
a foreign file or data source. See wfopen (p. 515) for details on the syntax for source_descrip-
tions and table_descriptions. Note, in particular, that you may use the byrow table_descrip-
tion for Excel 2007 files to instruct EViews to save the series by row (instead of the standard
by column).
Options
Workfile Save Options
The default workfile save settings use the global options.
wfsave Object Container, Data, and File Commands
1 Save using single precision.
2 Save using double precision.
c Save compressed workfile (not compatible with EViews
versions prior to 5.0).](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-536-320.jpg)
![532—Chapter 12. Command Reference
will save the two series GDP and UNEMP into the existing Excel 2007 file “macro.xlsx”,
specifying that the series should be written by row, starting in cell A1 on sheet Sheet2.
To save the latter file in a macro-enabled Excel 2007 file, you should specify the explicit file-
name extension “.XLSM”:
wfsave(type=excelxml, mode=update) macro.xlsm range="Sheet2!a1"
byrow @keep gdp unemp
The command:
wfsave “<mydropboxdrive>”foldernipa.wf1”
will save the file to the cloud location MYDROPBOXDRIVE.
Cross-references
See Chapter 3. “Workfile Basics,” on page 41 of the User’s Guide I for a discussion of work-
files.
See also pagesave (p. 442), wfopen (p. 515), and pageload (p. 440).
Make the selected workfile page the active workfile page.
Syntax
wfselect wfname[pgname]
where wfname is the name of a workfile that has been loaded into memory. You may option-
ally provide the name of a page in the new default workfile that you wish to be made active.
Examples
wfselect myproject
wfselect myprojectpage2
both change the default workfile to MYPROJECT. The first command uses the default active
page, while the second changes the page to PAGE2.
Cross-references
See Chapter 3. “Workfile Basics,” on page 41 of the User’s Guide I for a discussion of work-
files.
See also pageselect (p. 444).
wfselect Object Container, Data, and File Commands](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-539-320.jpg)
![wfunlink—533
Display the workfile statistics and summary view.
Syntax
wfstats [wfname]
displays the workfile statistics and summary view showing, for each page of the specified
workfile, the structure of the page, and a summary of the objects contained in the page. The
specified workfile must be open. If no workfile is specified, wfstats will display results for
the active workfile.
Examples
wfstats
displays the statistics view for the active workfile.
wfstats wf2
displays the statistics for the open workfile WF2.
Cross-references
See “Workfile Summary View” on page 75 of the User’s Guide I for a description of the
workfile statistics and summary view.
Break links in all link objects and auto-updating series (formulae) in the active workfile.
You should use some caution with this command as you will not be prompted before the
links and auto-updating series are converted.
Syntax
wfunlink
Examples
wfunlink
breaks links in all pages of the active workfile
wfstats Object Container, Data, and File Commands
wfunlink Object Container, Data, and File Commands](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-540-320.jpg)
![534—Chapter 12. Command Reference
Cross-references
See Chapter 8. “Series Links,” on page 223 of User’s Guide I for a description of link objects,
and “Auto-Updating Series” on page 193 of User’s Guide I for a discussion of auto-updating
series.
See also Link::link (p. 352) and Link::linkto (p. 353) in the Object Reference.
See also pageunlink (p. 450) and unlink (p. 501) for page and object based unlinking,
respectively.
Activates a workfile. If the workfile is currently open in EViews, then it is selected to
become the default workfile. If the workfile is not currently open, it is opened.
Syntax
wfuse [path]workfile_name[.wf1][page_name]
The name of the workfile is specified as the argument to wfopen and may be followed by an
optional page name to specify a specific page in the workfile. If the workfile is not located in
the current default directory, and is not currently open in EViews, you should specify the
path of the workfile along with its name. If you do specify a path, you should also include
the .WF1 extension.
Examples
wfuse basics
activates the BASICS workfile. If BASICS is not currently open in EViews and is not located
in the current default directory, wfuse will error.
wfuse c:mydatabasics.wf1
activates the BASICS workfile located in “c:mydata”.
wfuse c:mydatabasics.wf1page1
activates the page named PAGE1 in the BASICS workfile located in “C:MYDATA”.
Cross-references
See Chapter 3. “Workfile Basics,” on page 41 of the User’s Guide I for a discussion of work-
files.
See also wfopen (p. 515) and wfsave (p. 529).
wfuse Object Container, Data, and File Commands](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-541-320.jpg)
![write—535
Create or change workfiles.
No longer supported; provided for backward compatibility. This command has been
replaced by wfcreate (p. 510) and pageselect (p. 444).
Write EViews data to a text (ASCII), Excel, or Lotus file on disk.
Creates a foreign format disk file containing EViews data. May be used to export EViews
data to another program.
Unless you need to write your workfile data in transposed form, we recommend that you
use the more powerful command for writing a workfile page documented in pagesave
(p. 442).
Syntax
write(options) [path]filename arg1 [arg2 arg3 …]
Follow the keyword by a name for the output file and list the series to be written. The
optional path name may be on the local machine, or may point to a network drive. If the
path name contains spaces, enclose the entire expression in double quotation marks.
Note that EViews cannot, at present, write into an existing file. The file that you select will,
if it exists, be replaced.
Options
Options are specified in parentheses after the keyword and are used to specify the format of
the output file.
File type
If you omit the “t=” option, EViews will determine the type based on the file extension.
Unrecognized extensions will be treated as ASCII files. For Lotus and Excel spreadsheet files
workfile Object Container, Data, and File Commands
write Object Container, Data, and File Commands
prompt Force the dialog to appear from within a program.
t=dat, txt ASCII (plain text) files.
t=wk1, wk3 Lotus spreadsheet files.
t=xls Excel spreadsheet files.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-542-320.jpg)
![536—Chapter 12. Command Reference
specified without the “t=” option, EViews will automatically append the appropriate exten-
sion if it is not otherwise specified.
ASCII text files
Spreadsheet (Lotus, Excel) files
Examples
write(t=txt,na=.,d=c,dates) a:dat1.csv hat1 hat_se1
Writes the two series HAT1 and HAT_SE1 into an ASCII file named DAT1.CSV on the A
drive. The data file is listed by observations, NAs are coded as “.” (dot), each series is sepa-
rated by a comma, and the date/observation numbers are written together with the series
names.
write(t=txt,na=.,d=c,dates) dat1.csv hat1 hat_se1
writes the same file in the default directory.
Cross-references
See “Exporting to a Spreadsheet or Text File” on page 154 of User’s Guide I for a discussion.
See pagesave (p. 442) for a superior method of exporting workfile pages to foreign formats.
na=string Specify text string for NAs. Default is “NA”.
names (default) /
nonames
[Write / Do not write] series names.
dates / nodates [Write / Do not write] dates/obs. “Dates” is the default
unless the “t” option for writing by series is used, in which
case “nodates” is the default.
d=arg Specify delimiter (default is tab): “s” (space), “c”
(comma).
t Write by series. Default is to write by obs with series in col-
umns.
letter_number Coordinate of the upper-left cell containing data.
names (default) /
nonames
[Write / Do not write] series names.
dates (default) /
nodates
[Write / Do not write] dates/obs.
dates=arg Excel format for writing date: “first” (convert to the first
day of the corresponding observation if necessary), “last”
(convert to the last day of the corresponding observation).
t Write by series. Default is to write by obs with series in col-
umns.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-543-320.jpg)
![Financial Functions—551
Financial Functions
The following series-only functions permit you to perform calculations commonly employed
in financial analysis. The functions are evaluated for each observation in the workfile sam-
ple.
@pcha(x) one-period percentage change—
annualized (in decimal)
where is the lag associated with
one-year ( ) for quarterly
data, etc.).
@pcy(x) one-year percentage change (in
percent)
equals @pchy(x)*100
@pchy(x) one-year percentage change (in
decimal)
, where
is the lag associated with one-year
( ) for annual data, etc.).
Name Function Description
@fv(r,n,x[,fv,t]) future value future value of an -period annu-
ity with rate , payments , and
optional final lump sum payment
.
A non-zero value for the optional
indicates that the payments are
made at the beginning of periods
instead of ends.
@nper(r,x,pv[,fv,t]) number of periods number of annuity periods
required to produce the present
value with rate , payments
and optional final lump sum pay-
ment .
A non-zero value for the optional
indicates that the payments are
made at the beginning of periods.
@pv(r,n,x[,fv,t]) present value present value of an -period
annuity with rate , payments ,
and optional final lump sum pay-
ment .
A non-zero value for the optional
indicates that the payments are
made at the beginning of periods.
@pcha(x)
1 @pch x
n
1–
n
n 4
X X n– – X n– n
n 12
n
r x
fv
t
pv r x
fv
t
n
r x
fv
t](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-558-320.jpg)
![552—Chapter 13. Operator and Function Reference
Descriptive Statistics
These functions compute descriptive statistics for a specified sample, excluding missing val-
ues if necessary. The default sample is the current workfile sample. If you are performing
these computations on a series and placing the results into a series, you can specify a sam-
ple as the last argument of the descriptive statistic function, either as a string (in double
quotes) or using the name of a sample object. For example:
series z = @mean(x, "1945m01 1979m12")
or
w = @var(y, s2)
where S2 is the name of a sample object and W and X are series. Note that you may not use
a sample argument if the results are assigned into a matrix, vector, or scalar object. For
example, the following assignment:
vector(2) a
series x
a(1) = @mean(x, "1945m01 1979m12")
is not valid since the target A(1) is a vector element. To perform this latter computation, you
must explicitly set the global sample prior to performing the calculation performing the
assignment:
smpl 1945:01 1979:12
a(1) = @mean(x)
To determine the number of observations available for a given series, use the @obs function.
Note that where appropriate, EViews will perform casewise exclusion of data with missing
@pmt(r,n,pv[,fv,t]) payment amount payment amount for an -period,
present value annuity with rate
and optional final lump sum
payment .
A non-zero value for the optional
indicates that the payments are
made at the beginning of periods.
@rate(n,x,pv[,fv,t]) interest interest rate for an period annu-
ity with payments , present value
, and optional final lump sum
payment .
A non-zero value for the optional
indicates that the payments are
made at the beginning of periods.
n
pv
r
fv
t
n
x
pv
fv
t](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-559-320.jpg)
![Descriptive Statistics—553
values. For example, @cov(x,y) and @cor(x,y) will use only observations for which data
on both X and Y are valid.
In the following table, arguments in square brackets [ ] are optional arguments:
• [s]: sample expression in double quotes or name of a sample object. The optional
sample argument may only be used if the result is assigned to a series. For @quantile,
you must provide the method option argument in order to include the optional sample
argument.
If the desired sample expression contains the double quote character, it may be entered
using the double quote as an escape character. Thus, if you wish to use the equivalent of,
smpl if name = "Smith"
in your @MEAN function, you should enter the sample condition as:
series y = @mean(x, "if name=""Smith""")
The pairs of double quotes in the sample expression are treated as a single double quote.
Function Name Description
@cor(x,y[,s]) correlation the correlation between X and Y.
@cov(x,y[,s]) covariance the covariance between X and Y
(division by ).
@covp(x,y[,s]) population covari-
ance
the covariance between X and Y
(division by ).
@covs(x,y[,s]) sample covariance the covariance between X and Y
(division by ).
@gmean(x[,s]) geometric mean the geometric mean of X. The geo-
metric mean is calculated as the
exponential of the sum of the logs of
X.
@hmean(x[,s]) harmonic mean computes the harmonic mean of the
values of X. The harmonic mean is
calculated as the reciprocal of the
mean of the reciprocals of X.
@imax(x) maximum index workfile index of the maximum of
the values in X for the current sam-
ple.
@imin(x) minimum index workfile index of the maximum of
the values in X for the current sam-
ple.
@inner(x,y[,s]) inner product the inner product of X and Y.
@kurt(x[,s]) kurtosis kurtosis of values in X.
n
n
n 1–](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-560-320.jpg)
![554—Chapter 13. Operator and Function Reference
@mae(x,y[,s]) mean absolute error the mean of the absolute value of
the difference between X and Y.
@mape(x,y[,s]) mean absolute per-
centage error
100 multiplied by the mean of the
absolute difference between X and Y,
divided by Y.
@max(x[,s]) maximum maximum of the values in X.
@maxes(x,n[,s]) n-largest numbers maximum n values in X, arranged
largest to smallest, returned in a vec-
tor object. Note this function may
not be used for series generation.
@mean(x[,s]) mean average of the values in X.
@median(x[,s]) median computes the median of the X (uses
the average of middle two observa-
tions if the number of observations
is even).
@min(x[,s]) minimum minimum of the values in X.
@mins(x,n[,s]) n-smallest numbers minimum n values in X, arranged
smallest to largest, returned in a vec-
tor object. Note this function may
not be used for series generation.
@nas(x[,s]) number of NAs the number of missing observations
for X in the current sample.
@prod(x[,s]) product the product of the elements of X
(note this function is prone to
numerical overflows).
@obs(x[,s]) number of observa-
tions
the number of non-missing observa-
tions for X in the current sample.
@quantile(x,q[,m,s]) quantile the q-th quantile of the series X. m is
an optional string argument for
specifying the quantile method: “b”
(Blom), “r” (Rankit-Cleveland), “o”
(Ordinary), “t” (Tukey), “v” (van
der Waerden), “g” (Gumbel). The
default value is “r”.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-561-320.jpg)
![Descriptive Statistics—555
@ranks(x[,o,t,s]) ranks the ranking of each observation in
X.
The order of ranking is set using o:
“a” (ascending - default) or “d”
(descending).
Ties are broken according to the set-
ting of t: “i” (ignore), “f” (first), “l”
(last), “a” (average - default), “r”
randomize.
If you wish to specify tie-handling
options, you must also specify the
order option (e.g. ‘@ranks(x, “a”,
“i”)’).
@rmse(x,y[,s]) root mean square
error
the square root of the mean of the
squared difference between X and Y.
@skew(x[,s]) skewness skewness of values in X.
@smape(x,y[,s]) symmetric mean
absolute percentage
error
200 multiplied by the mean of the
absolute difference between X and Y
divided by the sum of the absolute
values of X and Y.
@stdev(x[,s]) standard deviation square root of the unbiased sample
variance (sum-of-squared residuals
divided by ).
@stdevp(x[,s]) population standard
deviation
square root of the population vari-
ance (sum-of-squared residuals
divided by ).
@stdevs(x[,s]) sample standard
deviation
square root of the unbiased sample
variance. Note this is the same cal-
culation as @stdev.
@sum(x[,s]) sum the sum of X.
@sumsq(x[,s]) sum-of-squares sum of the squares of X.
@theil(x,y[,s]) Theil inequality coef-
ficient
the root mean square error divided
by the sum of the square roots of the
means of X squared and Y squared.
@var(x[,s]) variance variance of the values in X (division
by ).
@varp(x[,s]) population variance variance of the values in X. Note this
is the same calculation as @var.
@vars(x[,s]) sample variance sample variance of the values in X
(division by ).
n 1–
n
n
n 1–](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-562-320.jpg)
![556—Chapter 13. Operator and Function Reference
Cumulative Statistic Functions
These functions perform basic running or cumulative statistics for a series and may be used
as part of a series expression. The functions are split into two types, those that cumulate for-
wards and those that cumulate backwards. The forwards cumulating functions return the
running values of a statistic from the start of the workfile (or optionally a sample) to the cur-
rent observation. The backwards cumulating functions return the running values from the
end of the workfile (or sample) to the current observation.
By default, EViews will use the entire workfile range when computing the statistics. You may
provide the optional sample s as a literal (quoted) sample expression or a named sample.
Missing values, NAs, do not propagate through these functions. Thus the cumulative sums
of the numbers 1, 3, 4, NA, 5 are 1, 4, 8, 8, 13.
Name Function Description
@cumsum(x[,s]) cumulative sum cumulative sum of the values in X from
the start of the workfile/sample.
@cumprod(x[,s]) cumulative product cumulative product of the values in X
from the start of the workfile/sample
(note this function could be subject to
numerical overflows).
@cummean(x[,s]) cumulative mean mean of the values in X from the start of
the workfile/sample to the current
observation.
@cumstdev(x[,s]) cumulative standard devia-
tion
sample standard deviation of the values
in X from the start of the workfile/sam-
ple to the current observation. Note this
calculation involves division by .
@cumstdevp(x[,s]) cumulative population stan-
dard deviation
population standard deviation of the
values in X from the start of the work-
file/sample to the current observation.
Note this calculation involves division
by .
@cumstdevs(x[,s]) cumulative sample standard
deviation
sample standard deviation of the values
in X from the start of the workfile/sam-
ple. Note this performs the same calcu-
lation as @cumstdev.
@cumvar(x[,s]) cumulative variance population variance of the values in X
from the start of the workfile/sample to
the current observation. Note this calcu-
lation involves division by .
n 1–
n
n](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-563-320.jpg)
![Cumulative Statistic Functions—557
@cumvarp(x[,s]) cumulative population vari-
ance
population variance of the values in X
from the start of the workfile/sample to
the current observation. Note this per-
forms the same calculation as @cumvar.
@cumvars(x[,s]) cumulative sample variance sample variance of the values in X from
the start of the workfile/sample to the
current observation. Note this calcula-
tion involves division by .
@cummax(x[,s]) cumulative maximum maximum of the values in X from the
start of the workfile/sample to the cur-
rent observation.
@cummin(x[,s]) cumulative minimum minimum of the values in X from the
start of the workfile/sample to the cur-
rent observation.
@cumsumsq(x[,s]) cumulative sum-of-squares sum of squares of the values in X from
the start of the workfile/sample to the
current observation.
@cumobs(x[,s]) cumulative nmber of non-
NA observations
the number of non-missing observations
in X from the start of the workfile/sam-
ple to the current observation.
@cumnas(x[,s]) cumulative number of NA
observations
the number of missing observations in X
from the start of the workfile/sample to
the current observation.
@cumquan-
tile(x,q[,s])
cumulative quantile the qth quantile in X computed from the
start of the workfile/sample to the cur-
rent observation. Quantiles are com-
puted using the Cleveland definition.
@cummedian(x[,s]) cumulative median the median of X computed from the end
of the workfile/sample to the current
observation.
@cumbsum(x[,s]) backwards cumulative sum cumulative sum of the values in X from
the end of the workfile/sample.
@cumbprod(x[,s]) backwards cumulative
product
cumulative product of the values in X
from the end of the workfile/sample
(note this function could be subject to
numerical overflows).
@cumbmean(x[,s]) backwards cumulative
mean
mean of the values in X from the end of
the workfile/sample to the current
observation.
@cumbstdev(x[,s]) backwards cumulative stan-
dard deviation
sample standard deviation of the values
in X from the end of the workfile/sam-
ple to the current observation. Note this
calculation involves division by .
n 1–
n 1–](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-564-320.jpg)
![558—Chapter 13. Operator and Function Reference
@cumbstdevp(x[,s]) backwards cumulative pop-
ulation standard deviation
population standard deviation of the
values in X from the end of the work-
file/sample to the current observation.
Note this calculation involves division
by .
@cumbstdevs(x[,s]) backwards cumulative sam-
ple standard deviation
sample standard deviation of the values
in X from the end of the workfile/sam-
ple. Note this performs the same calcu-
lation as @cumstdev.
@cumbvar(x[,s]) backwards cumulative vari-
ance
population variance of the values in X
from the end of the workfile/sample to
the current observation. Note this calcu-
lation involves division by
@cumbvarp(x[,s]) backwards cumulative pop-
ulation variance
population variance of the values in X
from the end of the workfile/sample to
the current observation. Note this per-
forms the same calculation as @cumvar.
@cumbvars(x[,s]) backwards cumulative sam-
ple variance
sample variance of the values in X from
the end of the workfile/sample to the
current observation. Note this calcula-
tion involves division by .
@cumbmax(x[,s]) backwards cumulative max-
imum
maximum of the values in X from the
end of the workfile/sample to the cur-
rent observation.
@cumbmin(x[,s]) backwards cumulative min-
imum
minimum of the values in X from the
end of the workfile/sample to the cur-
rent observation.
@cumbsumsq(x[,s]) backwards cumulative sum-
of-squares
sum of squares of the values in X from
the start of the workfile/sample to the
current observation.
@cumbobs(x[,s]) backwards cumulative
nmber of non-NA observa-
tions
the number of non-missing observations
in X from the end of the workfile/sam-
ple to the current observation.
@cumbnas(x[,s]) backwards cumulative
nmber of NA observations
the number of missing observations in X
from the end of the workfile/sample to
the current observation.
@cumbquan-
tile(x,q[,s])
backwards cumulative
quantile
the qth quantile in X computed from the
end of the workfile/sample to the cur-
rent observation. Quantiles are com-
puted using the Cleveland definition.
@cumbmedian(x[,s]) backwards cumulative
median
the median of X computed from the end
of the workfile/sample to the current
observation.
n
n
n 1–](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-565-320.jpg)
![By-Group Statistics—565
By-Group Statistics
The following “by group”-statistics are available in EViews. They may be used as part of a
series expression statements to compute the statistic for subgroups, and to assign the value
of the relevant statistic to each observation.
The functions take a series expression arg1 for which we wish to compute group statistics
and one or more series expressions arg2, defining the groups over which we wish to com-
pute the statistics. In the leading case, you will provide a single series expression containing
categories. More generally, you can provide several series expressions separated by commas
(arg2, arg3, ...) that jointly define the categories. For brevity, the table entries below only
depict a single categorical variable arg2.
Function Description
@obsby(arg1, arg2[, s]) Number of non-NA arg1 observations for each arg2
group.
@nasby(arg1, arg2[, s]) Number of arg1 NA values for each arg2 group.
@sumsby(arg1, arg2[, s]) Sum of arg1 observations for each arg2 group.
@meansby(arg1, arg2[, s]) Mean of arg1 observations for each arg2 group.
@minsby(arg1, arg2[, s]) Minimum value of arg1 observations for each arg2
group.
@maxsby(arg1, arg2[, s]) Maximum value of arg1 observations for each arg2
group.
@mediansby(arg1, arg2[, s]) Median of arg1 observations for each arg2 group.
@varsby(arg1, arg2[, s]) Variance of arg1 observations for each arg2 group
(division by ).
@varssby(arg1, arg2[, s]) Sample variance of arg1 observations for each arg2
group (division by ).
@varpsby(arg1, arg2[, s]) Variance of arg1 observations for each arg2 group
(division by ).
@stdevsby(arg1, arg2[, s]) Sample standard deviation of arg1 observations for
each arg2 group (division by ).
@stdevssby(arg1, arg2[, s]) Sample standard deviation of arg1 observations for
each arg2 group (division by ).
@stdevpsby(arg1, arg2[, s]) Standard deviation of arg1 observations for each
arg2 group (division by ).
@sumsqsby(arg1, arg2[, s]) Sum of squares of arg1 observations for each arg2
group.
@quantilesby(arg1, arg2,
arg3[, s])
Quantiles of arg1 observations for each arg2 group
where arg1 is the desired quantile.
n
n 1–
n
n 1–
n 1–
n](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-572-320.jpg)
![566—Chapter 13. Operator and Function Reference
With the exception of @quantileby, all of the functions take the form:
@STATBY(arg1, arg2[, arg3, arg4, ..., argn, s])
where @STATBY is one of the by-group function keyword names, arg1 is a series expression,
arg2 and the optional arg3 to argn are numeric or alpha series expression identifying the
subgroups, and s is an optional sample literal (a quoted sample expression) or a named sam-
ple. With the exception of @obsby, arg1 must be a numeric series.
By default, EViews will use the workfile sample when computing the descriptive statistics.
You may provide the optional sample s as a literal (quoted) sample expression or a named
sample.
The @quantileby function requires an additional argument for the quantile value that you
wish to compute:
@quantileby(arg1, arg2[, arg3, arg4, ..., argn], q[, s])
For example, to compute the first quartile, you should use the q value of 0.25.
There are two related functions of note,
@groupid(arg1[, arg2, arg3, arg4, ..., argn, smpl]])
returns an integer indexing the group ID for each observation of the series or alpha expres-
sion arg.
Special Functions
EViews provides a number of special functions used in evaluating the properties of various
statistical distributions or for returning special mathematical values such as Euler’s con-
stant. For further details on special functions, see the extensive discussions in Temme
(1996), Abramowitz and Stegun (1964), and Press, et al. (1992).
@skewsby(arg1, arg2[, s]) Skewness of arg1 observations for each arg2 group.
@kurtsby(arg1, arg2[, s]) Kurtosis of arg1 observations for each arg2 group.
@firstsby(arg1, arg2[, s]) First non-missing value in arg1 for each arg2 group
@lastsby(arg1, arg2[, s]) Last non-missing value in arg1 for each arg2 group
Function Description
@beta(a,b) beta integral (Euler integral of the second kind):
for .
B a b, t
a 1–
1 t–
b 1–
td
0
1
G a G b
G a b
------------------------
a b 0,](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-573-320.jpg)
![576—Chapter 13. Operator and Function Reference
Dated Workfile Information
The following functions return information about each observation in the workfile. They are
used to generate new series.
Function Name Description
@after(arg1) date is after creates a dummy variable equal to 1
if the observation is after or on the
date given by arg1. arg1 should be
enclosed in quotes.
@before(arg1) date is before creates a dummy variable equal to 1
if the observation is before the date
given by arg1. arg1 should be
enclosed in quotes.
@date date returns the date value.
@day day of month returns the day of the month.
@during(arg1 date is during creates a dummy variable equal to 1
if the observation lies between the
dates given by the date pair con-
tained in arg1, and 0 otherwise. arg1
should be given in quotes.
@enddate observations in range returns the end date of the period
of time associated with the obser-
vation in the workfile.
@event(arg1[,
basis])
event proportion proportion of a one-off event that
lies in each observation.
@holiday(arg1[,
basis])
holiday proportion proportion of an annual event that
lies in each observation.
@hour hour returns the hour of each observation
as an integer.
@hourf hour floating point returns the hour of each observation
as a floating point number (e.g., 9:30
a.m. returns 9.5, and 5:15 p.m.
returns 17.25).
@isperiod(x) period dummy returns a dummy variable for
whether the observation is in the
specified period.
@minute minutes returns the minute of each observa-
tion as an integer.
@month month returns the month of an observation.
@quarter quarter returns the quarter of an observa-
tion.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-583-320.jpg)
![Valmap Functions—577
Panel Workfile Functions
Valmap Functions
The following utility functions were designed to facilitate working with value mapped series.
Additional detail is provided in “Valmap Functions” on page 218 of the User’s Guide I
@seas(x) seasonal dummy returns a seasonal dummy variable.
@second seconds returns the second of each observa-
tion as an integer.
@strdate(x) string dates returns a string representing the date
for every observation.
@trend([x]) time trend returns a time trend using the work-
file calendar.
@trendc([x]) calendar time trend returns a time trend where the trend
uses calendar time.
@weekday day of the week returns the day of the week.
@year year returns the year
Function Name Description
@cellid element returns the inner dimension identi-
fier index.
@crossid observations in range returns the cross-section identifier
index.
@obsid cross-section observation
number
returns the observation number
within each panel cross section.
@trend time trend returns a time trend for each cross-
section using the observed dates in
the panel.
@trendc calendar time trend returns a time trend for each cross-
section where the trend uses calen-
dar time.
Function Name Description
@map(x[, mapname]) mapped value returns the mapped values of a
series.
@unmap(x, mapname) unmapped numeric
value
returns the numeric values associ-
ated with a set of string value labels.
@unmaptxt(x, mapname) unmapped text value returns the string values associated
with a set of string value labels.](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-584-320.jpg)
![580—Chapter 14. Operator and Function Listing
@between(x, val1, val2) Creates a dummy variable equal to 1 for observations where
the series is greater than or equal to val1 and less than or
equal to val2.
@binom(n,x) binomial coefficient
@binomlog(n,x) natural logarithm of the binomial coefficient
@cbeta(x,a,b) beta cumulative distribution (CDF)
@cbinom(x,n,p) binomial cumulative distribution (CDF)
@cchisq(x,v) chi-square cumulative distribution (CDF)
@ceiling(x) smallest integer not less than
@cellid element
@cexp(x,m) exponential cumulative distribution (CDF)
@cextreme(x) extreme value cumulative distribution (CDF)
@cfdist(x,v1,v2) F-distribution cumulative distribution (CDF)
@cgamma(x,b,r) gamma cumulative distribution (CDF)
@cged(x,r) generalized error cumulative distribution (CDF)
@chisq(x,v) chi-square p-value
@claplace(x) Laplace cumulative distribution (CDF)
@clogistic(x) logistic cumulative distribution (CDF)
@cloglog(x) complementary log-log function
@clognorm(x,m,s) log normal cumulative distribution (CDF)
@cnegbin(x,n,p) negative binomial cumulative distribution (CDF)
@cnorm(x) normal cumulative distribution (CDF)
@columns(g) number of columns.
@cor(x,y[,s]) correlation
@cos(x) cosine (argument in radians)
@cov(x,y[,s]) population covariance
@covp(x,y[,s]) population covariance
@covs(x,y[,s]) sample covariance
@cpareto(x,a,k) Pareto cumulative distribution (CDF)
@cpoisson(x,m) Poisson cumulative distribution (CDF)
@crossid observations in range
@ctdist(x,v) Student’s t-distribution cumulative distribution (CDF)
@cumbmax(x[,s]) backwards cumulative maximum
@cumbmean(x[,s]) backwards cumulative mean
Operator / Function Description](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-587-320.jpg)
![—581
@cumbmin(x[,s]) backwards cumulative minimum
@cumbnas(x[,s]) backwards cumulative nmber of NA observations
@cumbobs(x[,s]) backwards cumulative nmber of non-NA observations
@cumbprod(x[,s]) backwards cumulative product
@cumbstdev(x[,s]) backwards cumulative sample standard deviation
@cumbstdevp(x[,s]) backwards cumulative po](https://image.slidesharecdn.com/eviews9commandref-160512181308/85/E-views-9-command-ref-588-320.jpg)
Ad
More Related Content
What's hot (17)
Viewers also liked (14)
Ad
Similar to E views 9 command ref (20)
![Erpi admin 11123510[1] by иссам неязын issam hejazin](https://cdn.slidesharecdn.com/ss_thumbnails/erpiadmin111235101-140814022227-phpapp01-thumbnail.jpg?width=560&fit=bounds)
Ad
Recently uploaded (20)
E views 9 command ref
- 2. EViews 9 Command and Programming Reference
- 3. EViews 9 Command and Programming Reference Copyright © 1994–2015 IHS Global Inc. All Rights Reserved ISBN: 978-1-880411-29-2 This software product, including program code and manual, is copyrighted, and all rights are reserved by IHS Global Inc. The distribution and sale of this product are intended for the use of the original purchaser only. Except as permitted under the United States Copyright Act of 1976, no part of this product may be reproduced or distributed in any form or by any means, or stored in a database or retrieval system, without the prior written permission of IHS Global Inc. Disclaimer The authors and IHS Global Inc. assume no responsibility for any errors that may appear in this manual or the EViews program. The user assumes all responsibility for the selection of the pro- gram to achieve intended results, and for the installation, use, and results obtained from the pro- gram. Trademarks EViews® is a registered trademark of IHS Global Inc. Windows, Excel, PowerPoint, and Access are registered trademarks of Microsoft Corporation. PostScript is a trademark of Adobe Corpora- tion. X11.2 and X12-ARIMA Version 0.2.7, and X-13ARIMA-SEATS are seasonal adjustment pro- grams developed by the U. S. Census Bureau. Tramo/Seats is copyright by Agustin Maravall and Victor Gomez. Info-ZIP is provided by the persons listed in the infozip_license.txt file. Please refer to this file in the EViews directory for more information on Info-ZIP. Zlib was written by Jean-loup Gailly and Mark Adler. More information on zlib can be found in the zlib_license.txt file in the EViews directory. Bloomberg is a trademark of Bloomberg Finance L.P. All other prod- uct names mentioned in this manual may be trademarks or registered trademarks of their respec- tive companies. IHS Global Inc. 4521 Campus Drive, #336 Irvine CA, 92612-2621 Telephone: (949) 856-3368 Fax: (949) 856-2044 e-mail: [email protected] web: www.eviews.com March 14, 2016
- 4. Table of Contents PREFACE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 CHAPTER 1. OBJECT AND COMMAND BASICS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Command Capture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Using Commands . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Object Declaration and Initialization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 Object Commands . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 Object Data Members . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 Interactive Commands . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 Auxiliary Commands . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 CHAPTER 2. WORKING WITH GRAPHS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 Creating a Graph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 Changing Graph Types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 Customizing a Graph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 Labeling Graphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 Printing Graphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 Exporting Graphs to Files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 Graph Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 CHAPTER 3. WORKING WITH TABLES AND SPREADSHEETS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 Creating a Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 Assigning Table Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 Customizing Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 Labeling Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 Printing Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 Exporting Tables to Files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 Customizing Spreadsheet Views . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 Table Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 CHAPTER 4. WORKING WITH SPOOLS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 Creating a Spool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 Working with a Spool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 Printing the Spool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 Spool Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
- 5. ii—Table of Contents CHAPTER 5. STRINGS AND DATES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 Strings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .77 Dates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .94 CHAPTER 6. EVIEWS PROGRAMMING . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .117 Program Basics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .117 Simple Programs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .124 Program Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .126 Program Modes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .134 Program Arguments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .136 Program Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .137 Control of Execution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .138 Multiple Program Files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .148 Subroutines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .149 User-Defined Dialogs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .159 Version 4 Compatibility Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .169 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .173 CHAPTER 7. EXTERNAL CONNECTIVITY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .175 Reading EViews Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .175 EViews COM Automation Server . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .177 EViews COM Automation Client Support (MATLAB and R) . . . . . . . . . . . . . . . . . . . . . . . . . . .177 EViews Database Extension Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .184 CHAPTER 8. ADD-INS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .187 What is an Add-in? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .187 Getting Started with Add-ins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .187 Using Add-ins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .191 Add-ins Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .194 Managing Add-ins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .198 Creating an Add-in . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .201 Add-ins Design Support . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .209 CHAPTER 9. USER OBJECTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .213 What is a User Object? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .213 Unregistered User Objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .214 Registered User Objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .216 Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .219
- 6. Table of Contents—iii Managing User Object Classes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 228 Defining a Registered User Object Class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 230 User Object Programming Support . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 236 CHAPTER 10. USER-DEFINED OPTIMIZATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239 Defining the Objective and Controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239 The Optimize Command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241 Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245 Technical Details . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 250 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 256 CHAPTER 11. MATRIX LANGUAGE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 257 Declaring Matrix Objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 257 Assigning Matrix Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 258 Copying Data Between Objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261 Matrix Expressions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 268 Matrix Commands and Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 272 Matrix Views and Procs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 276 Matrix Operations versus Loop Operations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277 Summary of Automatic Resizing of Matrix Objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 278 CHAPTER 12. COMMAND REFERENCE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 281 CHAPTER 13. OPERATOR AND FUNCTION REFERENCE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 547 Operators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 548 Basic Mathematical Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 549 Time Series Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 550 Financial Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 551 Descriptive Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 552 Cumulative Statistic Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 556 Moving Statistic Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 559 Group Row Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 563 By-Group Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 565 Special Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 566 Trigonometric Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 569 Statistical Distribution Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 569 String Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 573 Date Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 573
- 7. iv—Table of Contents Indicator Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .573 Workfile & Informational Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .573 Valmap Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .577 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .578 CHAPTER 14. OPERATOR AND FUNCTION LISTING . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .579 CHAPTER 15. WORKFILE FUNCTIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .593 Basic Workfile Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .593 Dated Workfile Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .594 Panel Workfile Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .599 CHAPTER 16. SPECIAL EXPRESSION REFERENCE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .601 CHAPTER 17. STRING AND DATE FUNCTION REFERENCE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .611 CHAPTER 18. MATRIX LANGUAGE REFERENCE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .653 CHAPTER 19. PROGRAMMING LANGUAGE REFERENCE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .701 APPENDIX A. WILDCARDS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .739 Wildcard Expressions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .739 Using Wildcard Expressions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .739 Source and Destination Patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .740 Resolving Ambiguities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .741 Wildcard versus Pool Identifier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .742 INDEX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .745
- 8. Preface The EViews User’s Guide focuses primarily on interactive use of EViews using dialogs and other parts of the graphical user interface. Alternatively, you may use EViews’ powerful command and batch processing language to perform almost every operation that can be accomplished using the menus. You can enter and edit commands in the command window, or you can create and store the commands in programs that document your research project for later execution. This text, the EViews Command and Programming Reference, documents the use of com- mands in EViews, along with examples of commands for commonly performed operations, and provides general information about the command, programming, and matrix languages: The first chapter provides an overview of using commands in EViews: • Chapter 1. “Object and Command Basics,” on page 3 explains the basics of using commands to work with EViews objects, and provides examples of some commonly performed operations. The next set of chapters discusses commands for working with specific EViews objects: • Chapter 2. “Working with Graphs,” on page 33 describes the use of commands to cus- tomize graph objects. • Chapter 3. “Working with Tables and Spreadsheets,” on page 57 documents the table object and describes the basics of working with tables in EViews. • Chapter 4. “Working with Spools,” on page 69 discusses commands for working with spools. The EViews programming and matrix language are described in: • Chapter 5. “Strings and Dates,” on page 77 describes the syntax and functions avail- able for manipulating text strings and dates. • Chapter 6. “EViews Programming,” on page 117 describes the basics of using pro- grams for batch processing and documents the programming language. • Chapter 7. “External Connectivity,” on page 175 documents EViews features for interfacing with external applications through the OLEDB driver and various COM automation interfaces. • Chapter 11. “Matrix Language,” on page 257 describes the EViews matrix language. The remaining chapters contain reference material:
- 9. 2—Preface • Chapter 12. “Command Reference,” on page 281 is the primary reference for com- mands to work with EViews objects, workfiles, databases, external interfaces, pro- grams, as well as other auxiliary commands. • Chapter 13. “Operator and Function Reference,” on page 547 offers a categorical list of element operators, numerical functions and descriptive statistics functions that may be used with series and (in some cases) matrix objects. • Chapter 14. “Operator and Function Listing,” on page 579 contains an alphabetical list of the element operators, numerical functions and descriptive statistics functions that may be used with series and (in some cases) matrix objects. • Chapter 15. “Workfile Functions,” on page 593 describes special functions for obtain- ing information about observations in the workfile. • Chapter 16. “Special Expression Reference,” on page 601 describes special expressions that may be used in series assignment and generation, or as terms in estimation spec- ifications. • Chapter 17. “String and Date Function Reference,” on page 611 documents the library of string and date functions for use with alphanumeric and date values. • Chapter 18. “Matrix Language Reference,” on page 653 describes the functions and commands used in the EViews matrix language. • Chapter 19. “Programming Language Reference,” on page 701 documents the func- tions and keywords used in the EViews programming language. There is additional material in the appendix: • Appendix A. “Wildcards,” on page 739 describes the use of wildcards in different con- texts in EViews.
- 10. Chapter 1. Object and Command Basics This chapter provides an brief overview of the command method of working with EViews and EViews objects. The command line interface of EViews is comprised of a set of single line commands, each of which may be classified as one of the following: • object declarations and assignment statements. • object view and procedure commands. • interactive commands for creating objects and displaying views and procedures. • auxiliary commands. The following sections provide an overview of each of the command types. But before dis- cussing the various types, we offer a brief discussion of the interactive and batch methods of using commands in EViews. Command Capture Before beginning our in-depth discussion of commands in EViews, we note that a great way to familiarize yourself with the EViews command language is to use command capture. With command capture, when you perform an operation using the user-interface, EViews will save the equivalent text command for display and export. EViews offers command capture for most object views and procedures, and a large number of interactive operations. To enable command capture you must display the command capture window. To display the window or set focus on the window, click on Window/Display Command Capture Win- dow from the main EViews menu.
- 11. 4—Chapter 1. Object and Command Basics Once opened, you may move and resize the capture window as desired. The window may even be pinned or moved outside of the frame of the EViews application (see “Command and Capture Window Docking” on page 6). Additionally, you may choose to echo any captured commands to the command window. To enable this feature, select Options/General Options from the manu menu, and click on the Command Capture node, and click on the Capture to Command Window checkbox. You can copy-and-paste the contents of the capture window, or you can save the contents to a file. Right-clicking in the window brings up a menu for copying or clearing the window, saving the contents to a file on disk, or opening a new, untitled program containing the con- tents of the window. Note that not all interactive operations in EViews are capture enabled. Among the notable exceptions are some types of graph creation and customization, and individual cell editing for tables and spreadsheets. In addition, capture of object view graph customization is not supported. Thus, if you wish to capture the commands for customizing the impulse response view of a VAR object, you should freeze the view, and then customize the frozen graph object.
- 12. Using Commands—5 Using Commands Commands may be used interactively or executed in batch mode. Interactive Use The command window is located (by default) just below the main menu bar at the top of the EViews window. A blinking insertion cursor in the command window indicates that key- board focus is in the command window and that keystrokes will be entered in the window at the insertion point. If no insertion cursor is present, simply click in the command window to change the focus. To work interactively, you will type a command into the command window, then press ENTER to execute the command. If you enter an incomplete command, EViews will open a dialog box prompting you for additional information. A command that you enter in the window will be executed as soon as you press ENTER. The insertion point need not be at the end of the com- mand line when you press ENTER. EViews will execute the entire line that contains the insertion point. The contents of the command area may also be saved directly into a text file for later use. First make certain that the command window is active by clicking anywhere in the window, and then select File/Save As… from the main menu. EViews will prompt you to save an ASCII file in the default working directory (default name “commandlog.txt”) containing the entire contents of the command window. Command Window Editing When you enter a command, EViews will add it to the list of previously executed commands contained in the window. You can scroll up to an earlier command, edit it, and hit ENTER. The modified command will be executed. You may also use standard Windows copy-and- paste between the command window and any other window. EViews offers a couple of specialized tools for displaying previous commands. First, to bring up previous commands in the order they were entered, press the Control key and the UP arrow (CTRL+UP). The last command will be entered into the command window. Holding down the CTRL key and pressing UP repeatedly will display the next prior commands. Repeat until the desired command is displayed.
- 13. 6—Chapter 1. Object and Command Basics To look at a history of commands, press the Control Key and the J key (CTRL+J). This key combination displays a history window containing the last 30 commands executed. Use the UP and DOWN arrows until the desired command is selected and then press the ENTER key to add it to the command window, or simply double click on the desired command. To close the history window without selecting a command, click elsewhere in the command window or press the Escape (ESC) key. To execute the retrieved command, simply press ENTER again. You may first edit the com- mand if you wish to do so. You may resize the command window so that a larger number of previously executed com- mands are visible. Use the mouse to move the cursor to the bottom of the window, hold down the mouse button, and drag the bottom of the window downwards. Command and Capture Window Docking The EViews 9 command and capture windows are dockable, hideable, and floatable. Dockable and hideable windows allow you to move frequently used windows out of the way while keeping them close at hand. They offer space saving convenience which is particu- larly valued when working with smaller screen devices like laptops. Floatable windows allow you to move them out of the way of your work. You may even go so far as to float a window outside of the EViews frame. To re-arrange the layout of the Command or Capture window, first make sure the window pane is not in sliding mode (the Pin icon should be IN so that it is vertical).
- 14. Using Commands—7 Then drag the window to the desired location or dock. Note that it is possible to drag a window into an unwanted position where it covers a por- tion of a window with which you wish to work. In most cases, you may simply move the window out of the way. If it is difficult to drag the window because you cannot see the title- bar, you should be able use the scroll bars to gain easier access to the titlebar of the window. Floating You can drag the window to a new location just like any other window in EViews. However, unlike other EViews windows, You can drag the command window outside of the EViews frame: Docking Both the command and capture window panes can now be docked on any side inside the main EViews window. Begin dragging the pane by left-clicking title bar and holding it down as you drag it off the edge. At that point, you will see small docking guides appear inside the main EViews window:
- 15. 8—Chapter 1. Object and Command Basics To dock the window, you will drag it to one of the docking guides. The docking behavior will depend on which guide you select. The docking guides allow you to dock the window pane to one of eight pre-defined areas. The four guides on the outer edge of the main window allow you to dock the window as the primary pane while the inner guides allow you to dock the window as a secondary pane. Pri- mary panes take over the entire length of the selected edge and force other docked panes that could intersect to become smaller to compensate. Secondary panes only take over the portion of the selected edge that is not already occupied. For example, if the capture pane is dragged to the right-most docking guide like this:
- 16. Using Commands—9 and then released, the final layout looks like this Because it is primary, it pushes the docked Command window pane at the top of the win- dow to become smaller. Alternatively, if the inner right guide was selected instead like this:
- 17. 10—Chapter 1. Object and Command Basics the final layout would look like this Docking two panes onto the same primary edge can result in stacking, with the recently moved pane being adjacent to the edge (as the primary), and any secondary panes being stacked next to the primary pane (in the order they were docked).
- 18. Using Commands—11 You can also dock a pane inside another pane in order to use the share the same space. If you drag a pane over another pane, you are presented with additional docking guides inside the other pane:
- 19. 12—Chapter 1. Object and Command Basics For example, selecting the inner-right guide area allows the Capture pane to appear to the right of the Command pane with an adjustable split bar to let you resize the split: The docking guide also offers an additional guide area directly in the center that allows you to convert both panes into a tabbed view:
- 20. Using Commands—13 Pinning If your window is docked, you can “pin” it by clicking on the pin icon (so that it is horizon- tal). When pinned, the window will be minimized and a small tab will be displayed in the docked location. To expand the pinned window, simply click on the tab. The window will automatically con- tracts when it loses focus. Clicking on the Pin icon again will “un-pin” and expand the win- dow permanently. Keyboard Focus We note that as you open and close object windows in EViews, the keyboard focus may change from the command window to the active window. If you then wish to enter a com- mand, you will first need to click in the command window to set the focus. You can influ- ence EViews’ method of choosing keyboard focus by changing the global defaults—simply select Options/General Options.../Window Behavior in the main menu, and change the Keyboard Focus setting as desired. Batch Program Use You may assemble a number of commands into a program, and then execute the commands in batch mode. Each command in the program will be executed in the order that it appears in the program. Using batch programs allows you to make use of advanced capabilities such as looping and condition branching, and subroutine and macro processing. Programs also are an excellent way to document a research project since you will have a record of each
- 21. 14—Chapter 1. Object and Command Basics step of the project. Batch program use of EViews is discussed in greater detail in Chapter 6. “EViews Programming,” on page 117. One way to create a program file in EViews is to select File/New/Program. EViews will open an untitled program window into which you may enter your commands. You can save the program by clicking on the Save or SaveAs button, navigating to the desired directory, and entering a file name. EViews will append the extension “.PRG” to the name you provide. Alternatively, you can use your favorite text (ASCII) editor to create a program file contain- ing your commands. It will prove convenient to name your file using the extension “.PRG”. The commands in this program may then be executed from within EViews. You may also enter commands in the command window and then use File/Save As... to save the log for editing. Object Declaration and Initialization The simplest types of commands create an EViews object, or assign data to or initialize an existing object. Object Declaration A simple object declaration has the form object_type(options) object_name where object_name is the name you would like to give to the newly created object and object_type is one of the following object types: Alpha (p. 4) Pool (p. 454) Sym (p. 699) Coef (p. 16) Rowvector (p. 501) System (p. 729) Equation (p. 31) Sample (p. 522) Table (p. 770) Factor (p. 179) Scalar (p. 529) Text (p. 802) Graph (p. 230) Series (p. 535) User (p. 810) Group (p. 284) Spool (p. 662) Valmap (p. 819) Logl (p. 361) Sspace (p. 633) Var (p. 827)
- 22. Object Declaration and Initialization—15 Details on each of the commands associated with each of these objects are provided in the section beginning on the specified page in the Object Reference. For example, the declaration, series lgdp creates a new series called LGDP, while the command: equation eq1 creates a new equation object called EQ1. Matrix objects are typically declared with their dimension as an option provided in paren- theses after the object type. For example: matrix(5,5) x creates a matrix named X, while coef(10) results creates a 10 element coefficient vector named RESULTS. Simple declarations initialize the object with default values; in some cases, the defaults have a meaningful interpretation, while in other cases, the object will simply be left in an incom- plete state. In our examples, the newly created LGDP will contain all NA values and X and RESULTS will be initialized to 0, while EQ1 will be simply be an uninitialized equation con- taining no estimates. Note that in order to declare an object you must have a workfile currently open in EViews. You may open or create a workfile interactively from the File Menu or drag-and-dropping a file onto EViews (see Chapter 3. “Workfile Basics,” on page 41 of User’s Guide I for details), or you can may use the wfopen (p. 515) command to perform the same operations inside a program. Object Assignment Object assignment statements are commands which assign data to an EViews object using the “=” sign. Object assignment statements have the syntax: object_name = expression where object_name identifies the object whose data is to be modified and expression is an expression which evaluates to an object of an appropriate type. Note that not all objects per- Matrix (p. 378) String (p. 685) Vector (p. 869) Model (p. 414) Svector (p. 692) 5 5
- 23. 16—Chapter 1. Object and Command Basics mit object assignment; for example, you may not perform assignment to an equation object. (You may, however, initialize the equation using a command method.) The nature of the assignment varies depending on what type of object is on the left hand side of the equal sign. To take a simple example, consider the assignment statement: x = 5 * log(y) + z where X, Y and Z are series. This assignment statement will take the log of each element of Y, multiply each value by 5, add the corresponding element of Z, and, finally, assign the result into the appropriate element of X. Similarly, if M1, M2, and M3 are matrices, we may use the assignment statement: m1 = @inverse(m2) * m3 to postmultiply the matrix inverse of M2 by M3 and assign the result to M1. This statement presumes that M2 and M3 are suitably conformable. Object Modification In cases where direct assignment using the “=” operator is not allowed, one may initialize the object using one or more object commands. We will discuss object commands in greater detail in a moment (see “Object Commands,” on page 17) but for now simply note that object commands may be used to modify the contents of an existing object. For example: eq1.ls log(cons) c x1 x2 uses an object command to estimate the linear regression of the LOG(CONS) on a constant, X1, and X2, and places the results in the equation object EQ1. sys1.append y=c(1)+c(2)*x sys1.append z=c(3)+c(4)*x sys1.ls adds two lines to the system specification, then estimates the specification using system least squares. Similarly: group01.add gdp cons inv g x adds the series GDP, CONS, INV, G, and X to the group object GROUP01. More on Object Declaration Object declaration may often be combined with assignment or command initialization. For example: series lgdp = log(gdp)
- 24. Object Commands—17 creates a new series called LGDP and initializes its elements with the log of the series GDP. Similarly: equation eq1.ls y c x1 x2 creates a new equation object called EQ1 and initializes it with the results from regressing the series Y against a constant term, the series X1 and the series X2. Lastly: group group01 gdp cons inv g x create the group GROUP01 containing the series GDP, CONS, INV, G, and X. An object may be declared multiple times so long as it is always declared to be of the same type. The first declaration will create the object, subsequent declarations will have no effect unless the subsequent declaration also specifies how the object is to be initialized. For example: smpl @first 1979 series dummy = 1 smpl 1980 @last series dummy=0 creates a series named DUMMY that has the value 1 prior to 1980 and the value 0 thereafter. Redeclaration of an object to a different type is not allowed and will generate an error. Object Commands Most of the commands that you will employ are object commands. An object command is a command which displays a view of or performs a procedure using a specific object. Object commands have two main parts: an action followed by a view or procedure specification. The (optional) display action determines what is to be done with the output from the view or procedure. The view or procedure specification may provide for options and arguments to modify the default behavior. The complete syntax for an object command has the form: action(action_opt) object_name.view_or_proc(options_list) arg_list where: action....................is one of the four verb commands (do, freeze, print, show). action_opt ............an option that modifies the default behavior of the action. object_name ..........the name of the object to be acted upon. view_or_proc .........the object view or procedure to be performed. options_list ...........an option that modifies the default behavior of the view or proce- dure.
- 25. 18—Chapter 1. Object and Command Basics arg_list ................ a list of view or procedure arguments. Action Commands There are four possible action commands: • show displays the object view in a window. • do executes procedures without opening a window. If the object’s window is not cur- rently displayed, no output is generated. If the objects window is already open, do is equivalent to show. • freeze creates a table or graph from the object view window. • print prints the object view window. As noted above, in most cases, you need not specify an action explicitly. If no action is spec- ified, the show action is assumed for views and the do action is assumed for most proce- dures (though some procedures will display newly created output in new windows unless the command was executed via a batch program). For example, when using an object command to display the line graph series view, EViews implicitly adds a show command. Thus, the following two lines are equivalent: gdp.line show gdp.line In this example, the view_or_proc argument is line, indicating that we wish to view a line graph of the GDP data. There are no additional options or arguments specified in the com- mand. Alternatively, for the equation method (procedure) ls, there is an implicit do action: eq1.ls cons c gdp do eq1.ls cons c gdp so that the two command lines describe equivalent behavior. In this case, the object com- mand will not open the window for EQ1 to display the result. You may display the window by issuing an explicit show command after issuing the initial command: show eq1 or by combining the two commands: show eq1.ls cons c gdp Similarly: print eq1.ls cons c gdp both performs the implicit do action and then sends the output to the printer. The following lines show a variety of object commands with modifiers:
- 26. Object Commands—19 show gdp.line print(l) group1.stats freeze(output1) eq1.ls cons c gdp do eq1.forecast eq1f The first example opens a window displaying a line graph of the series GDP. The second example prints (in landscape mode) descriptive statistics for the series in GROUP1. The third example creates a table named OUTPUT1 from the estimation results of EQ1 for a least squares regression of CONS on GDP. The final example executes the forecast procedure of EQ1, putting the forecasted values into the series EQ1F and suppressing any procedure out- put. Of these four examples, only the first opens a window and displays output on the screen. Output Control As noted above, the display action determines the destination for view and procedure out- put. Here we note in passing a few extensions to these general rules. You may request that a view be simultaneously printed and displayed on your screen by including the letter “p” as an option to the object command. For example, the expression, gdp.correl(24, p) is equivalent to the two commands: show gdp.correl(24) print gdp.correl(24) since correl is a series view. The “p” option can be combined with other options, sepa- rated by commas. So as not to interfere with other option processing, we recommend that the “p” option always be specified after any required options. Note that the print command accepts the “l” or “p” option to indicate landscape or portrait orientation. For example: print(l) gdp.correl(24) Printer output can be redirected to a text file, frozen output, or a spool object. (See output (p. 428), and the discussion in “Print Setup” on page 833 of User’s Guide I for details.) The freeze command used without options creates an untitled graph or table from a view specification: freeze gdp.line You also may provide a name for the frozen object in parentheses after the word freeze. For example: freeze(figure1) gdp.bar
- 27. 20—Chapter 1. Object and Command Basics names the frozen bar graph of GDP as “figure1”. View and Procedure Commands Not surprisingly, the view or procedure commands correspond to elements of the views and procedures menus for the various objects. For example, the top level of the view menu for the series object allows you to: display a spreadsheet view of the data, graph the data, perform a one-way tabulation, compute and display a correlogram or long-run variance, perform unit root or variance ratio tests, conduct a BDS independence test, or display or modify the label view. Object commands exist for each of these views. Suppose for example, that you have the series object SER01. Then: ser01.sheet ser01.stats display the spreadsheet and descriptive statistics views of the data in the series. There are a number of graph commands corresponding to the menu entry, so that you may enter: ser01.line ser01.bar ser01.hist which display a line graph, bar graph, and histogram, respectively, of the data in SER01. Similarly, ser01.freq performs a one-way tabulation of the data, and: ser01.correl ser01.lrvar ser01.uroot ser01.vratio 2 4 8 ser01.bdstest display the correlogram and long-run variances, and conduct unit root, variance ratio, and independence testing for the data in the series. Lastly: ser01.label(r) "this is the added series label" appends the text “this is the added series label” to the end of the remarks field. There are commands for all of the views and procedures of each EViews object. Details on the syntax of each of the object commands may be found in Chapter 1. “Object View and Procedure Reference,” beginning on page 2 in the Object Reference.
- 28. Object Data Members—21 Object Data Members Every object type in EViews has a selection of data members. These members contain infor- mation about the object and can be retrieved from an object to be used as part of another command, or stored into the workfile as a new object. Data members can be accessed by typing the object name followed by a period and then the data member name. Note that all data members’ names start with an “@” symbol. The following data members belong to every object type in EViews: Along with these global data members, each object type has a set of data members specific to that type. For example, equation objects have a data member, @r2, that returns a scalar containing the R-squared from that equation. Groups have an member, @count, that returns a scalar containing the number of series contained within that group. A full list of each object’s data members can be found under the object’s section in Chapter 1. “Object View and Procedure Reference,” on page 2 of the Object Reference. As an example of using data members, the commands: equation eq1.ls y c x1 x2 table tab1 tab1(1,1) = eq1.@f Data Member Name Description @name Returns the name of the object @displayname Returns the display name of the object. If the object has no display name, the name is returned @type Returns the object type @units Returns the units of the object, (if available) @source Returns the source of the object (if available) @description Returns the description of the object (if available) @remarks Returns the remarks of the object (if available) @updatetime Returns the string representa- tion of the time the object was last updated
- 29. 22—Chapter 1. Object and Command Basics create an equation named EQ1 and a table named TAB1, and then set the first cell of the table equal to the F-statistic from the estimated equation. Interactive Commands There is also a set of auxiliary commands which are designed to facilitate interactive use. These commands perform the same operations as equivalent object commands, but do so on newly created, unnamed objects. For example, the command: ls y c x1 x2 will regress the series Y against a constant term, the series X1 and the series X2, and create a new untitled equation object to hold the results. Similarly, the command: scat x y creates an untitled group object containing the series X and Y and then displays a scatterplot of the data in the two series. Since these commands are designed primarily for interactive use, they are designed for car- rying out simple tasks. Overuse of these interactive tools, or their use in programs, will make it difficult to manage your work since unnamed objects cannot be referenced by name from within a program, cannot be saved to disk, and cannot be deleted except through the graphical Windows interface. In general, we recommend that you use named objects rather than untitled objects for your work. For example, we may replace the first auxiliary com- mand above with the statement: equation eq1.ls y c x1 x2 to create the named equation object EQ1. This example uses declaration of the object EQ1 and the equation method ls to perform the same task as the auxiliary command above. Similarly, group mygroup x y mygroup.scat displays the scatterplot of the series in the named group MYGROUP. Auxiliary Commands Auxiliary commands are commands which are unrelated to a particular object (i.e., are not object views or procs), or act on an object in a way that is generally independent of the type or contents of the object. Many of the important auxiliary commands are used for managing objects, and object containers. A few of the more important commands are described below. Auxiliary commands typically follow the syntax:
- 30. Auxiliary Commands—23 command(option_list) argument_list where command is the name of the command, option_list is a list of options separated by commas, and argument_list is a list of arguments generally separated by spaces. An example of an auxiliary command is: store(d=c:newdatadb1) gdp m x which will store the three objects GDP, M and X in the database named DB1 in the directory C:NEWDATA. Managing Workfiles and Databases There are two types of object containers: workfiles and databases. All EViews objects must be held in an object container, so before you begin working with objects you must create a workfile or database. Workfiles and databases are described in depth in Chapter 3. “Workfile Basics,” beginning on page 41 and Chapter 10. “EViews Databases,” beginning on page 307 of User’s Guide I. Managing Workfiles To declare and create a new workfile, you may use the wfcreate (p. 510) command. You may enter the keyword wfcreate followed by a name for the workfile, an option for the fre- quency of the workfile, and the start and end dates. The most commonly used workfile fre- quency type options are: but there are additional options for multi-year, bimonthly, fortnight, ten-day, daily with cus- tom week, intraday, integer date, and undated frequency workfiles. For example: wfcreate macro1 q 1965Q1 1995Q4 creates a new quarterly workfile named MACRO1 from the first quarter of 1965 to the fourth quarter of 1995. wfcreate cps88 u 1 1000 a annual. s semi-annual. q quarterly. m monthly. w weekly. d daily (5 day week). 7 daily (7 day week). u undated/unstructured.
- 31. 24—Chapter 1. Object and Command Basics creates a new undated workfile named CPS88 with 1000 observations. Alternately, you may use wfopen (p. 515) to read a foreign data source into a new workfile. If you have multiple open workfiles, the wfselect (p. 532) command may be used to change the active workfile. To save the active workfile, use the wfsave (p. 529) command by typing the keyword wfsave followed by a workfile name. If any part of the path or workfile name has spaces, you should enclose the entire expression in quotation marks. The active workfile will be saved in the default path under the given name. You may optionally provide a path to save the workfile in a different directory: wfsave a:mywork If necessary, enclose the path name in quotations. To close the workfile, use the close (p. 310) command. For example: close mywork closes the workfile window of MYWORK. To open a previously saved workfile, use the wfopen (p. 515) command. You should follow the keyword with the name of the workfile. You can optionally include a path designation to open workfiles that are not saved in the default path. For example: wfopen "c:myworkproj1" Managing Databases To create a new database, follow the dbcreate (p. 342) command keyword with a name for the new database. Alternatively, you could use the db (p. 340) command keyword followed by a name for the new database. The two commands differ only when the named database already exists. If you use dbcreate and the named database already exists on disk, EViews will error indi- cating that the database already exits. If you use db and the named database already exists on disk, EViews will simply open the existing database. Note that the newly opened data- base will become the default database. For example: dbcreate mydata1 creates a new database named MYDATA1 in the default path, opens a new database win- dow, and makes MYDATA1 the default database. db c:evdatausdb
- 32. Auxiliary Commands—25 opens the USDB database in the specified directory if it already exists. If it does not, EViews creates a new database named USDB, opens its window, and makes it the default database. You may use dbopen (p. 344) to open an existing database and to make it the default data- base. For example: dbopen findat opens the database named FINDAT in the default directory. If the database does not exist, EViews will error indicating that the specified database cannot be found. You may use dbrename to rename an existing database. Follow the dbrename keyword by the current (old) name and a new name: dbrename temp1 newmacro To delete an existing database, use the dbdelete (p. 344) command. Follow the dbdelete keyword by the name of the database to delete: dbdelete c:datausmacro dbcopy (p. 340) makes a copy of the existing database. Follow the dbcopy keyword with the name of the source file and the name of the destination file: dbcopy c:evdatamacro1 a:macro1 dbpack (p. 346) and dbrebuild (p. 347) are database maintenance commands. See also Chapter 10. “EViews Databases,” beginning on page 307 of User’s Guide I for a detailed description. Managing Objects In the course of a program you will often need to manage the objects in a workfile by copy- ing, renaming, deleting and storing them to disk. EViews provides a number of auxiliary commands which perform these operations. The following discussion introduces you to the most commonly used commands; a full description of these, and other commands is pro- vided in Chapter 12. “Command Reference,” on page 281. Copying Objects You may create a duplicate copy of one or more objects using the copy (p. 327) command. The copy command is an auxiliary command with the format: copy source_name dest_name where source_name is the name of the object you wish to duplicate, and dest_name is the name you want attached to the new copy of the object. The copy command may also be used to copy objects in databases and to move objects between workfiles and databases.
- 33. 26—Chapter 1. Object and Command Basics Copy with Wildcard Characters EViews supports the use of wildcard characters (“?” for a single character match and “*” for a pattern match) in destination specifications when using the copy and rename commands. Using this feature, you can copy or rename a set of objects whose names share a common pattern in a single operation. This features is useful for managing series produced by model simulations, series corresponding to pool cross-sections, and any other situation where you have a set of objects which share a common naming convention. A destination wildcard pattern can be used only when a wildcard pattern has been provided for the source, and the destination pattern must always conform to the source pattern in that the number and order of wildcard characters must be exactly the same between the two. For example, the patterns: conform to each other. These patterns do not: When using wildcards, the destination name is formed by replacing each wildcard in the destination pattern by the characters from the source name that matched the corresponding wildcard in the source pattern. Some examples should make this principle clear: Note, as shown in the second example, that a simple asterisk for the destination pattern does not mean to use the unaltered source name as the destination name. To copy objects between containers preserving the existing name, either repeat the source pattern as the des- tination pattern, Source Pattern Destination Pattern x* y* *c b* x*12? yz*f?abc Source Pattern Destination Pattern a* b *x ?y x*y* *x*y* Source Pattern Destination Pattern Source Name Destination Name *_base *_jan x_base x_jan us_* * us_gdp gdp x? x?f x1 x1f *_* **f us_gdp usgdpf ??*f ??_* usgdpf us_gdp
- 34. Auxiliary Commands—27 copy x* db1::x* or omit the destination pattern entirely: copy x* db1:: If you use wildcard characters in the source name and give a destination name without a wildcard character, EViews will keep overwriting all objects which match the source pattern to the name given as destination. For additional discussion of wildcards, see Appendix A. “Wildcards,” on page 739. Renaming Objects You can give an object a different name using the rename (p. 461) command. The rename command has the format: rename source_name dest_name where source_name is the original name of the object and dest_name is the new name you would like to give to the object. rename can also be used to rename objects in databases. You may use wildcards when renaming series. The name substitution rules are identical to those described above for copy. Deleting Objects Objects may be removed from the workfile or a database using the delete command. The delete command has the format: delete name_pattern where name_pattern can either be a simple name such as “XYZ”, or a pattern containing the wildcard characters “?” and “*”, where “?” means to match any one character, and “*” means to match zero or more characters. When a pattern is provided, all objects in the workfile with names matching the pattern will be deleted. Appendix A. “Wildcards,” on page 739 describes further the use of wildcards. Saving Objects All named objects will be saved automatically in the workfile when the workfile is saved to disk. You can store and retrieve the current workfile to and from disk using the wfsave (p. 529) and wfopen (p. 515) commands. Unnamed objects will not be saved as part of the workfile. You can also save objects for later use by storing them in a database. The store (p. 484) command has the format: store(option_list) object1 object2 …
- 35. 28—Chapter 1. Object and Command Basics where object1, object2, ..., are the names of the objects you would like to store in the data- base. If no options are provided, the series will be stored in the current default database (see Chapter 10. “EViews Databases,” on page 307 of User’s Guide I for a discussion of the default database). You can store objects into a particular database by using the option “d=db_name” or by prepending the object name with a database name followed by a dou- ble colon “::”, such as: store db1::x db2::x Fetch Objects You can retrieve objects from a database using the fetch (p. 355) command. The fetch command has the same format as the store command: fetch(option_list) object1 object2 … To specify a particular database use the “d=” option or the “::” extension as for store.
- 37. 30—Chapter 1. Object and Command Basics
- 39. 32—Chapter 1. Object and Command Basics
- 40. Chapter 2. Working with Graphs EViews provides an extensive set of commands to generate and customize graphs from the command line or using programs. A summary of the graph commands described below may be found under “Graph” on page 230 of the Object Reference. In addition, Chapter 15. “Graph Objects,” on page 705 of User’s Guide I describes graph cus- tomization in detail, focusing on the interactive method of working with graphs. Creating a Graph There are three types of graphs in EViews: graphs that are views of other objects, and named or unnamed graph objects. The commands provided for customizing the appearance of your graphs are available for use with named graph objects. You may use the dialogs interactively to modify the appearance of all types of graphs. Displaying graphs using commands The simplest way to display a graph view is to use one of the basic graph commands. (“Graph Creation Commands” on page 285 provides a convenient listing.) Where possible EViews will simply open the object and display the appropriate graph view. For example, to display a line or bar graph of the series INCOME and CONS, you may simply issue the commands: line income bar cons In other cases, EViews must first create an unnamed object and then will display the desired view of that object. For example: scat x y z first creates an unnamed group object containing the three series and then, using the scat view of a group, displays scatterplots of Y on X and Z on X in a single frame. As with other EViews commands, graph creation commands allow you to specify a variety of options and arguments to modify the default graph settings. You may, for example, rotate the bar graph using the “rotate” option, bar(rotate) cons or you may display boxplots along the borders of your scatter plot using: scat(ab=boxplot) x y z
- 41. 34—Chapter 2. Working with Graphs Note that while using graph commands interactively may be quite convenient, these com- mands are not recommended for program use since you will not be able to use the resulting unnamed objects in your program. The next section describes a more flexible approach to displaying graphs. Displaying graphs as object views You may display a graph of an existing object using a graph view command. For example, you may use the following two commands to display graph views of a series and a group: ser2.area(n) grp6.xypair The first command plots the series SER2 as an area graph with normalized scaling. The sec- ond command provides an XY line graph view of the group GRP6, with the series plotted in pairs. To display graphs for multiple series, we may first create a group containing the series and then display the appropriate view: group g1 x y z g1.scat shows the scatterplot of the series in the newly created group G1. There are a wide range of sophisticated graph views that you may display using commands. See Chapter . “,” beginning on page 895 of the Object Reference for a detailed listing along with numerous examples. Before proceeding, it is important to note that graph views of objects differ from graph objects in important ways: • First, graph views of objects may not be customized using commands after they are first created. The graph commands for customizing an existing graph are designed for use with graph objects. • Second, while you may use interactive dialogs to customize an existing object’s graph view, we caution you that there is no guarantee that the customization will be perma- nent. In many cases, the customized settings will not be saved with the object and will be discarded when the view changes or if the object is closed and then reopened. In contrast, graph objects may be customized extensively after they are created. Any customization of a graph object is permanent, and will be saved with the object. Since construction of a graph view is described in detail elsewhere (Chapter . “,” beginning on page 895 of the Object Reference), we focus the remainder of our attention on the creation and customization of graph objects.
- 42. Creating a Graph—35 Creating graph objects from object views If you wish to create a graph object from another object, you should combine the object view command with the freeze command. Simply follow the freeze keyword with an optional name for the graph object, and the object view to be frozen. For example, freeze grp6.xypair(m) creates and displays an unnamed graph object of the GRP6 view showing an XY line graph with the series plotted in pairs in multiple graph frames. Be sure to specify any desired graph options (e.g., “m”). Note that freezing an object view will not necessarily copy the existing custom appearance settings such as line color, axis assignment, etc. For this reason that we recommend that you create a graph object before performing extensive customiza- tion of a view. You should avoid creating unnamed graphs when using commands in programs since you will be unable to refer to, or work with the resulting object in a program. Instead, you should tell EViews to create a named object, as in: freeze(graph1) grp6.line which creates a graph object GRAPH1 containing a line graph of the data in GRP6. By default, the frozen graph will have updating turned off, but in most cases you may use the Graph::setupdate graph proc to turn updating on. Note that using the freeze command with a name for the graph will create the graph object and store it in the workfile without showing it. Furthermore, since we have frozen a graph type (line) that is different from our current XY line view, existing custom appearance set- tings will not be copied to the new graph. Once you have created a named graph object, you may use the various graph object procs to further customize the appearance of your graph. See “Customizing a Graph,” beginning on page 38. Creating named graph objects There are three direct methods for creating a named graph object. First, you may use the freeze command as described above. Alternatively, you may declare a graph object using the graph command. The graph command may be used to create graph objects with a spe- cific graph type or to merge existing graph objects. Specifying a graph by type To specify a graph by type you should use the graph keyword, followed by a name for the graph, the type of graph you wish to create, and a list of series (see “Graph Type Com- mands” on page 230 of the Object Reference for a list of types). If a type is not specified, a line graph will be created.
- 43. 36—Chapter 2. Working with Graphs For example, both: graph gr1 ser1 ser2 graph gr2.line ser1 ser2 create graph objects containing the line graph view of SER1 and SER2, respectively. Similarly: graph gr3.xyline group3 creates a graph object GR3 containing the XY line graph view of the series in GROUP3. Each graph type provides additional options, which may be included when declaring the graph. Among the most important options are those for controlling scaling or graph type. The scaling options include: • Automatic scaling (“a”), in which series are graphed using the default single scale. The default is left scale for most graphs, or left and bottom for XY graphs. • Dual scaling without crossing (“d”) scales the first series on the left and all other series on the right. The left and right scales will not overlap. • Dual scaling with possible crossing (“x”) is the same as the “d” option, but will allow the left and right scales to overlap. • Normalized scaling (“n”), scales using zero mean and unit standard deviation. For example, the commands: graph g1.xyline(d) unemp gdp inv show g1 create and display an XY line graph of the specified series with dual scales and no crossing. The graph type options include: • Mixed graph (“l”) creates a single graph in which the first series is the selected graph type (bar, area, or spike) and all remaining series are line graphs. • Multiple graph (“m”) plots each graph in a separate frame. • Stacked graph (“s”) plots the cumulative addition of the series, so the value of a series is represented as the difference between the lines, bars, or areas. For example, the commands: group grp1 sales1 sales2 graph grsales.bar(s) grp1 show grsales
- 44. Changing Graph Types—37 create a group GRP1 containing the series SALES1 and SALES2, then create and display a stacked bar graph GRSALES of the series in the group. You should consult the command reference entry for each graph type for additional informa- tion, including a list of the available options (i.e., see bar for complete details on bar graphs, and line for details on line graphs). Merging graph objects The graph command may also be used to merge existing named graph objects into a named multiple graph object. For example: graph gr2.merge gr1 grsales creates a multiple graph object GR2, combining two graph objects previously created. Creating unnamed graph objects There are two ways of creating an unnamed graph object. First, you may use the freeze command as described in “Creating graph objects from object views” on page 35. As we have seen earlier you may also use any of the graph type keywords as a command (“Displaying graphs using commands” on page 33). Follow the keyword with any available options for that type, and a list of the objects to graph. EViews will create an unnamed graph of the specified type that is not stored in the workfile. For instance: line(x) ser1 ser2 ser3 creates a line graph with series SER1 scaled on the left axis and series SER2 and SER3 scaled on the right axis. If you later decide to name this graph, you may do so interactively by clicking on the Name button in the graph button bar. Alternatively, EViews will prompt you to name or delete any unnamed objects before closing the workfile. Note that there is no way to name an unnamed graph object in a program. We recommend that you avoid creating unnamed graphs in programs since you will be unable to use the resulting object. Changing Graph Types You may change the graph type of a named graph object by following the object name with the desired graph type keyword and any options for that type. For example: grsales.bar(l) converts the bar graph GRSALES, created above, into a mixed bar-line graph, where SALES1 is plotted as a bar graph and SALES2 is plotted as a line graph within a single graph.
- 45. 38—Chapter 2. Working with Graphs Note that specialized graphs, such as boxplots, place limitations on your ability to change the graph type. In general, your ability to customize the graph settings is more limited when changing graph types than when generating the graph from the original data. Graph options are generally preserved when changing graph types. This includes attributes such as line color and axis assignment, as well as objects added to the graph, such as text labels, lines and shading. Commands to modify the appearance of named graph objects are described in “Customizing a Graph” on page 38. Note, however, that the line and fill graph settings are set independently. Line attributes apply to line and spike graphs, while fill attributes apply to bar, area, and pie graphs. For example, if you have modified the color of a line in a spike graph, this color will not be used for the fill area if the graph is changed to an area graph. Customizing a Graph EViews provides a wide range of tools for customizing the appearance of a named graph object. Nearly every display characteristic of the graph may be modified, including the appearance of lines and filled areas, legend characteristics and placement, frame size and attributes, and axis settings. In addition, you may add text labels, lines, and shading to the graph. You may modify the appearance of a graph using dialogs or via the set of commands described below. Note that the commands are only available for graph objects since they take the form of graph procedures. An overview of the relationship between the tabs of the graph dialog and the associated graph commands is illustrated below:
- 46. Customizing a Graph—39 Line characteristics For each data line in a graph, you may modify color, width, pattern and symbol using the Graph::setelem command. Follow the command keyword with an integer representing the data element in the graph you would like to modify, and one or more keywords for the characteristic you wish to change. List of symbol and pattern keywords, color keywords, and RGB settings are provided in Graph::setelem. To modify line color and width you should use the lcolor and lwidth keywords: graph gr1.line ser1 ser2 ser3 gr1.setelem(3) lcolor(orange) lwidth(2) gr1.setelem(3) lcolor(255, 128, 0) lwidth(2) The first command creates a line graph GR1 with colors and widths taken from the global defaults, while the latter two commands equivalently change the graph element for the third series to an orange line 2 points wide. Each data line in a graph may be drawn with a line, symbols, or both line and symbols. The drawing default is given by the global options, but you may elect to add lines or symbols using the lpattern or symbol keywords. To add circular symbols to the line for element 3, you may enter: gr1.setelem(3) symbol(circle) Note that this operation modifies the existing options for the symbols, but that the line type, color and width settings from the original graph will remain. To return to line only or sym- bol only in a graph in which both lines and symbols are displayed, you may turn off either symbols or patterns, respectively, by using the “none” type: gr1.setelem(3) lpat(none) or gr1.setelem(3) symbol(none) The first example removes the line from the drawing for the third series, so only the circular symbol is used. The second example removes the symbol, so only the line is used. If you attempt to remove the lines or symbols from a graph element that contains only lines or symbols, respectively, the graph will change to show the opposite type. For example: gr1.setelem(3) lpat(dash2) symbol(circle) gr1.setelem(3) symbol(none) gr1.setelem(3) lpat(none) initially represents element 3 with both lines and symbols, then turns off symbols for ele- ment 3 so that it is displayed as lines only, and finally shows element 3 as symbols only, since the final command turns off lines in a line-only graph.
- 47. 40—Chapter 2. Working with Graphs The examples above describe customization of the basic elements common to most graph types. “Modifying Boxplots” on page 52 provides additional discussion of Graph::setelem options for customizing boxplot data elements. Use of color with lines and filled areas By default, EViews automatically formats graphs to accommodate output in either color or black and white. When a graph is sent to a printer or saved to a file in black and white, EViews translates the colored lines and fills seen on the screen into an appropriate black and white representation. The black and white lines are drawn with line patterns, and fills are drawn with gray shading. Thus, the appearance of lines and fills on the screen may differ from what is printed in black and white (this color translation does not apply to boxplots). You may override this auto choice display method by changing the global defaults for graphs. You may choose, for example, to display all lines and fills as patterns and gray shades, respectively, whether the graph uses color or not. All subsequently created graphs will use the new settings. Alternatively, if you would like to override the color, line pattern, and fill settings for a given graph object, you may use the Graph::options graph proc. Color To change the color setting for an existing graph object, you should use options with the color keyword. If you wish to turn off color altogether for all lines and filled areas, you should precede the keyword with a negative sign, as in: gr1.options -color To turn on color, you may use the same command with the “-” omitted. Lines and patterns To always display solid lines in your graph, irrespective of the color setting, you should use options with the linesolid keyword. For example: gr1.options linesolid sets graph GR1 to use solid lines when rendering on the screen in color and when printing, even if the graph is printed in black and white. Note that this setting may make identifica- tion of individual lines difficult in a printed black and white graph, unless you change the widths or symbols associated with individual lines (see “Line characteristics” on page 39). Conversely, you may use the linepat option to use patterned lines regardless of the color setting: gr1.options linepat
- 48. Customizing a Graph—41 One advantage of using the linepat option is that it allows you to see the pattern types that will be used in black and white printing without turning off color in your graph. For exam- ple, using the Graph::setelem command again, change the line pattern of the second series in GR1 to a dashed line: gr1.setelem(2) lpat(dash1) This command will not change the appearance of the colored lines on the screen if color is turned on and auto choice of line and fill type is set. Thus, the line will remain solid, and the pattern will not be visible until the graph is printed in black and white. To view the cor- responding patterns, either turn off color so all lines are drawn as black patterned lines, or use the linepat setting to force patterns. To reset the graph or to override modified global settings so that the graph uses auto choice, you may use the lineauto keyword: gr1.options lineauto This setting instructs the graph to use solid lines when drawing in color, and use line pat- terns and gray shades when drawing in black and white. Note that regardless of the color or line pattern settings, you may always view the selected line patterns in the Lines & Symbols section of the graph options dialog. The dialog can be brought up interactively by double clicking anywhere in the graph. Filled area characteristics You can modify the color, gray shade, and hatch pattern of each filled area in a bar, area, or pie graph. To modify these settings, use Graph::setelem, followed by an integer representing the data element in the graph you would like to modify, and a keyword for the characteristic you wish to change. For example, consider the commands: graph mygraph.area(s) series1 series2 series3 mygraph.setelem(1) fcolor(blue) hatch(fdiagonal) gray(6) mygraph.setelem(1) fcolor(0, 0, 255) hatch(fdiagonal) gray(6) The first command creates MYGRAPH, a stacked area graph of SERIES1, SERIES2, and SERIES3. The latter two commands are equivalent, modifying the first series by setting its fill color to blue with a forward diagonal hatch. If MYGRAPH is viewed without color, the area will appear with a hatched gray shade of index 6. See Graph::setelem for a list of available color keywords, and for gray shade indexes and available hatch keywords. Note that changes to gray shades will not be visible in the graph unless color is turned off.
- 49. 42—Chapter 2. Working with Graphs Using preset lines and fills For your convenience, EViews provides you with a collection of preset line and fill character- istics. Each line preset defines a color, width, pattern, and symbol for a line, and each fill preset defines a color, gray shade, and hatch pattern for a fill. There are thirty line and thirty fill presets. The global graph options are initially set to use the EViews preset settings. These global options are used when you first create a graph, providing a different appearance for each line or fill. The first line preset is applied to the first data line, the second preset is applied to the second data line, and so on. If your graph contains more than thirty lines or fills, the pre- sets are simply reused in order. You may customize the graph defaults in the global Graph Options dialog. Your settings will replace the existing EViews defaults, and will be applied to all graphs created in the future. EViews allows you to use either the original EViews presets, or those you have specified in the global Graph Options dialog when setting the characteristics of an existing graph. The keyword preset is used to indicate that you should use the set of options from the corre- sponding EViews preset; the keyword default is used to indicate that you should use the set of options from the corresponding global graph element defaults. For example: mygraph.setelem(2) preset(3) allows the second fill area in MYGRAPH to use the original EViews presets for a third fill area. In current versions of EViews, these settings include a green fill, a medium gray shade of 8, and no hatch. Alternatively: mygraph.setelem(2) default(3) also changes the second area of MYGRAPH, but uses the third set of user-defined presets. If you have not yet modified your global graph defaults, the two commands will yield identical results. When using the preset or default keywords with boxplots, the line color of the specified preset will be applied to all boxes, whiskers, and staples in the graph. See “Modifying Box- plots” on page 52 for additional information. Scaling and axes There are four commands that may be used to modify the axis and scaling characteristics of your graphs: • First, the Graph::setelem command with the axis keyword may be used to assign data elements to different axes.
- 50. Customizing a Graph—43 • Second, the Graph::axis command can be used to customize the appearance of any axes in the graph object. You may employ the axis command to modify the scaling of the data itself, for example, as when you use a logarithmic scale, or to alter the scaling of the axis, as when you enable dual scaling. The axis command may also be used to change the appearance of axes, such as to modify tick marks, change the font size of axis labels, turn on grid or zero lines, or duplicate axes. • Third, the Graph::datelabel command modifies the labeling of the bottom date/ time axis in time plots. Use this command to change the way date labels are formatted or to specify label frequency. • Finally, the Graph::setobslabel command may be used to create custom axis labels for the observation scale of a graph. Assigning data to an axis In most cases, when a graph is created, all data elements are initially assigned to the left axis. XY graphs differ slightly in that data elements are initially assigned to either the left or bottom axis. Once a graph is created, individual elements may generally be assigned to either the left or right axis. In XY graphs, you may reassign individual elements to either the left, right, top, or bottom axis, while in boxplots or stacked time/observation graphs all data elements must be assigned to the same vertical axis. To assign a data element to a different axis, use the setelem command with the axis key- word. For example, the commands: graph graph02.line ser1 ser2 graph02.setelem(2) axis(right) first create GRAPH02, a line graph of SER1 and SER2, and then turn GRAPH02 into a dual scaled graph by assigning the second data element, SER2, to the right axis. In this example, GRAPH02 uses the default setting for dual scale graphs by disallowing crossing, so that the left and right scales do not overlap. To allow the scales to overlap, use the axis command with the overlap keyword, as in: graph02.axis overlap The left and right scales now span the entire axes, allowing the data lines to cross. To reverse this action and disallow crossing, use -overlap, (the overlap keyword preceded by a minus sign, “–”). For XY graphs without pairing, the first series is generally plotted along the bottom axis, and the remaining series are plotted on the left axis. XY graphs allow more manipulation than time/observation plots, because the top and bottom axes may also be assigned to an ele- ment. For example:
- 51. 44—Chapter 2. Working with Graphs graph graph03.xyline s1 s2 s3 s4 graph03.setelem(1) axis(top) graph03.setelem(2) axis(right) first creates an XY line graph GRAPH03 of the series S1, S2, S3, and S4. The first series is then assigned to the top axis, and the second series is moved to the right axis. Note that the graph now uses three axes: top, left, and right. Note that the element index in the setelem command is not necessary for boxplots and stacked time/observation graphs, since all data elements must be assigned to the same ver- tical axis. While EViews allows dual scaling for the vertical axes in most graph types, the horizontal axes must use a single scale on either the top or bottom axis. When a new element is moved to or from one of the horizontal axes, EViews will, if necessary, reassign elements as required so that there is a single horizontal scale. For example, using the graph created above, the command: graph03.setelem(3) axis(bottom) moves the third series to the bottom axis, forcing the first series to be reassigned from the top to the left axis. If you then issue the command: graph03.setelem(3) axis(right) EViews will assign the third series to the right axis as directed, with the first (next available element, starting with the first) series taking its place on the horizontal bottom axis. If the first element is subsequently moved to a vertical axis, the second element will take its place on the horizontal axis, and so forth. Note that series will never be reassigned to the right or top axis, so that series that placed on the top or right axis and subsequently reassigned will not be replaced automatically. For XY graphs with pairing, the same principles apply. However, since the elements are graphed in pairs, there is a set of elements that should be assigned to the same horizontal axis. You can switch which set is assigned to the horizontal using the axis keyword. For example: graph graph04.xypair s1 s2 s3 s4 graph03.setelem(1) axis(left) creates an XY graph that plots the series S1 against S2, and S3 against S4. Usually, the default settings assign the first and third series to the bottom axis, and the second and fourth series to the left axis. The second command line moves the first series (S1) from the bottom to the left axis. Since S1 and S3 are tied to the same axis, the S3 series will also be assigned to the left axis. The second and fourth series (S2 and S4) will take their place on the bottom axis.
- 52. Customizing a Graph—45 Modifying the data axis The Graph::axis command may be used to change the way data is scaled on an axis. To rescale the data, specify the axis you wish to change and use one of the following keywords: linear, linearzero (linear with zero included in axis), log (logarithmic), norm (stan- dardized). For example: graph graph05.line ser1 ser2 graph05.axis(left) log creates a line graph GRAPH05 of the series SER1 and SER2, and changes the left axis scaling method to logarithmic. The interaction of the data scales (these are the left and right axes for non-XY graphs) can be controlled using axis with the overlap keyword. The overlap keyword controls the overlap of vertical scales, where each scale has at least one series assigned to it. For instance: graph graph06.line s1 s2 graph06.setelem(2) axis(right) graph06.axis overlap first creates GRAPH06, a line graph of series S1 and S2, and assigns the second series to the right axis. The last command allows the vertical scales to overlap. The axis command may also be used to change or invert the endpoints of the data scale, using the range or invert keywords: graph05.axis(left) -invert range(minmax) inverts the left scale of GRAPH05 (“–” indicates an inverted scale) and sets its endpoints to the minimum and maximum values of the data. Modifying the date/time axis EViews automatically determines an optimal set of labels for the bottom axis of time plots. If you wish to modify the frequency or date format of the labels, you should use the Graph::datelabel command. Alternately, to create editable labels on the observation scale, use the Graph::setobslabel command. To control the number of observations between labels, use datelabel with the interval keyword to specify a desired step size. The stand-alone step size keywords include: auto (use EViews' default method for determining step size), ends (label first and last observa- tions), and all (label every observation). For example, mygraph.datelabel interval(ends) labels only the endpoints of MYGRAPH. You may also use a step size keyword in conjunc- tion with a step number to further control the labeling. These step size keywords include:
- 53. 46—Chapter 2. Working with Graphs obs (one observation), year (one year), m (one month), and q (one quarter), where each keyword determines the units of the number specified in the step keyword. For example, to label every ten years, you may specify: mygraph.datelabel interval(year, 10) In addition to specifying the space between labels, you may indicate a specific observation to receive a label. The step increment will then center around this observation. For example: mygraph.datelabel interval(obs, 10, 25) labels every tenth observation, centered around the twenty-fifth observation. You may also use datelabel to modify the format of the dates or change their placement on the axis. Using the format or span keywords, mygraph02.datelabel format(yy) -span formats the labels so that they display as two digit years, and disables interval spanning. If interval spanning is enabled, labels will be centered between the applicable tick marks. If spanning is disabled, labels are placed directly on the tick marks. For instance, in a plot of monthly data with annual labeling, the labels may be centered over the twelve monthly ticks (spanning enabled) or placed on the annual tick marks (spanning disabled). If your axis labels require further customization, you may use the setobslabel command to create a set of custom labels. mygraph.setobslabel(current) "CA" "OR" "WA" creates a set of axis labels, initializing each with the date or observation number and assigns the labels “CA”, “OR”, and “WA” to the first three observations. To return to EViews automatic labeling, you may use the clear option: mygraph.setobslabel(clear) Customizing axis appearance You may customize the appearance of tick marks, modify label font size, add grid lines, or duplicate axes labeling in your graph using Graph::axis. Follow the axis keyword with a descriptor of the axis you wish to modify and one or more arguments. For instance, using the ticksin, minor, and font keywords: mygraph.axis(left) ticksin -minor font(10) The left axis of MYGRAPH is now drawn with the tick marks inside the graph, no minor ticks, and a label font size of 10 point. To add lines to a graph, use the grid or zeroline keywords: mygraph01.axis(left) -label grid zeroline
- 54. Customizing a Graph—47 MYGRAPH01 hides the labels on its left axis, draws horizontal grid lines at the major ticks, and draws a line through zero on the left scale. In single scale graphs, it is sometimes desirable to display the axis labels on both the left and right hand sides of the graph. The mirror keyword may be used to turn on or off the dis- play of duplicate axes. For example: graph graph06.line s1 s2 graph06.axis mirror creates a line graph with both series assigned to the left axis (the default assignment), then turns on mirroring of the left axis to the right axis of the graph. Note that in the latter com- mand, you need not specify an axis to modify, since mirroring sets both the left and right axes to be the same. If dual scaling is enabled, mirroring will be overridden. In our example, assigning a data ele- ment to the right axis: graph06.setelem(1) axis(right) will override axis mirroring. Note that if element 1 is subsequently reassigned to the left scale, mirroring will again be enabled. To turn off mirroring entirely, simply precede the mir- ror keyword with a minus sign. The command: graph06.axis -mirror turns off axis mirroring. Customizing the graph frame The graph frame is used to set the basic graph proportions and display characteristics that are not part of the main portion of the graph. Graph size The graph frame size and proportions may be modified using the Graph::options com- mand. Simply specify a width and height using the size keyword. For example: testgraph.options size(5,4) resizes the frame of TESTGRAPH to virtual inches. Other frame characteristics The Graph::options command may also be used to modify the appearance of the graph area and the graph frame. A variety of modifications are possible. First, you may change the background colors in your graph, by using the “fillcolor” and “backcolor” keywords to change the frame fill color and the graph background color, respec- tively. The graph proc command: 5 4
- 55. 48—Chapter 2. Working with Graphs testgraph.options fillcolor(gray) backcolor(white) fills the graph frame with gray, and sets the graph area background color to white. Here we use the predefined color settings (“blue,” “red,” “green,” “black,” “white,” “purple,” “orange,” “yellow,” “gray,” “ltgray”); alternately, you may specify “color” with three argu- ments corresponding to the respective RGB settings. You may control display of axes frames. To select which axes should have a frame, you should use the “frameaxes” keyword: testgraph.options frameaxes(labeled) which turns off the frame on any axis which is not associated with data. Similarly: testgraph.options frameaxes(lb) draws a frame on the left and bottom axes only. By default, EViews uses the entire width of the graph for plotting data. If you wish to indent the data display from the edges of the graph frame, you should use the “indenth” (indent horizontal) or “indentv” (indent vertical) keywords: testgraph.options indenth(.05) indentv(0.1) indents the data by 0.05 inches horizontally, and 0.10 inches vertically from the edge of the graph frame. The options command also allows you to add and modify grid lines in your graph. For exam- ple: testgraph.options gridb -gridl gridpat(dash2) gridcolor(red) turns on dashed, red, vertical gridlines from the bottom axis, while turning off left scale gri- dlines. Labeling data values Bar and pie graphs allow you to label the value of your data within the graph. Use the Graph::options command with one of the following keywords: barlabelabove, barla- belinside, or pielabel. For example: mybargraph.options barlabelabove places a label above each bar in the graph indicating its data value. Note that the label will be visible only when there is sufficient space in the graph. Outlining and spacing filled areas EViews draws a black outline around each bar or area in a bar or area graph, respectively. To disable the outline, use options with the outlinebars or outlineareas keyword: mybargraph.options -outlinebars
- 56. Customizing a Graph—49 Disabling the outline is useful for graphs whose bars are spaced closely together, enabling you to see the fill color instead of an abundance of black outlines. EViews attempts to place a space between each bar in a bar graph. This space disappears as the number of bars increases. You may remove the space between bars by using the bar- space keyword: mybargraph.options -barspace Modifying the Legend A legend's location, text, and appearance may be customized. Note that single series graphs and special graph types such as boxplots and histograms use text objects for labeling instead of a legend. These text objects may only be modified interactively by double-clicking on the object to bring up the text edit dialog. To change the text string of a data element for use in the legend, use the Graph::name com- mand: graph graph06.line ser1 ser2 graph06.name(1) Unemployment graph06.name(2) DMR The first line creates a line graph GRAPH06 of the series SER1 and SER2. Initially, the legend shows “SER1” and “SER2”. The second and third command lines change the text in the leg- end to “Unemployment” and “DMR”. Note that the name command is equivalent to using the Graph::setelem command with the legend keyword. For instance, graph06.setelem(1) legend(Unemployment) graph06.setelem(2) legend(DMR) produces the same results. To remove a label from the legend, you may use name without providing a text string: graph06.name(2) removes the second label “DMR” from the legend. For an XY graph, the name command modifies any data elements that appear as axis labels, in addition to legend text. For example: graph xygraph.xy ser1 ser2 ser3 ser4 xygraph.name(1) Age xygraph.name(2) Height creates an XY graph named XYGRAPH of the four series SER1, SER2, SER3, and SER4. “SER1” appears as a horizontal axis label, while “SER2,” “SER3,” and “SER4” appear in the
- 57. 50—Chapter 2. Working with Graphs legend. The second command line changes the horizontal label of the first series to “Age”. The third line changes the second series label in the legend to “Height”. To modify characteristics of the legend itself, use Graph::legend. Some of the primary options may be set using the inbox, position and columns keywords. Consider, for exam- ple, the commands: graph graph07.line s1 s2 s3 s4 graph07.legend -inbox position(botleft) columns(4) The first line creates a line graph of the four series S1, S2, S3, and S4. The second line removes the box around the legend, positions the legend in the bottom left corner of the graph window, and specifies that four columns should be used for the text strings of the leg- end. When a graph is created, EViews automatically determines a suitable number of columns for the legend. A graph with four series, such as the one created above, would likely display two columns of two labels each. The columns command above, with an argument of four, cre- ates a long and slender legend, with each of the four series in its own column. You may also use the legend command to change the font size or to disable the legend completely: graph07.legend font(10) graph07.legend -display Note that if the legend is hidden, any changes to the text or position of the legend remain, and will reappear if the legend is displayed again. Adding text to the graph Text strings can be placed anywhere within the graph window. Using the Graph::addtext command: graph07.addtext(t) Fig 1: Monthly GDP adds the text “Fig 1: Monthly GDP” to the top of the GRAPH07 window. You can also use specific coordinates to specify the position of the upper left corner of the text. For example: graph08.addtext(.2, .1, x) Figure 1 adds the text string “Figure 1” to GRAPH08. The text is placed 0.2 virtual inches in, and 0.1 virtual inches down from the top left corner of the graph frame. The “x” option instructs EViews to place the text inside a box. An existing text object can be edited interactively by double-clicking on the object to bring up a text edit dialog. The object may be repositioned by specifying new coordinates in the dialog, or by simply dragging the object to its desired location.
- 58. Customizing a Graph—51 Adding lines and shading You may wish to highlight or separate specific areas of your graph by adding a line or shaded area to the interior of the graph using the Graph::draw command. Specify the type of line or shade option (line or shade), which axis it should be attached to (left, right, bottom, top) and its position. For example: graph09.draw(line, left) 5.2 draws a horizontal line at the value 5.2 on the left axis. Alternately: graph09.draw(shade, left) 4.8 5.6 draws a shaded horizontal area bounded by the values 4.8 and 5.6 on the left axis. You can also specify color, line width, and line pattern: graph09.draw(line, bottom, color(blue), width(2), pattern(3)) 1985:1 draws a vertical blue dashed line of width two points at the date “1985:1” on the bottom axis. Color may be specified using one or more of the following options: color(n1, n2, n3), where the arguments correspond to RGB settings, or color(keyword), where keyword is one of the predefined color keywords (“blue”, “red”, “green”, “black”, “white”, “purple”, “orange”, “yellow”, “gray”, “ltgray”). Using graphs as templates After customizing a graph as described above, you may wish to use your custom settings in another graph. Using a graph template allows you to copy the graph type, line and fill set- tings, axis scaling, legend attributes, and frame settings of one graph into another. This enables a graph to adopt all characteristics of another graph—everything but the data itself. To copy custom line or fill settings from the global graph options, use the preset or default keywords of the Graph::setelem command (as described in “Using preset lines and fills” on page 42). Modifying an existing graph To modify a named graph object, use the template command: graph10.template customgraph This command copies all the appearance attributes of CUSTOMGRAPH into GRAPH10. To copy text labels, lines and shading in the template graph in addition to all other option settings, use the “t” option: graph10.template(t) customgraph This command copies any text or shading objects that were added to the template graph using the Graph::addtext or Graph::draw commands or the equivalent steps using dia-
- 59. 52—Chapter 2. Working with Graphs logs. Note that using the “t” option overwrites any existing text and shading objects in the target graph. Using a template during graph creation All graph type commands also provide a template option for use when creating a new graph. For instance: graph mygraph.line(o = customgraph) ser1 ser2 creates the graph MYGRAPH of the series SER1 and SER2, using CUSTOMGRAPH as a tem- plate. The “o” option instructs EViews to copy all but the text, lines, and shading of the tem- plate graph. To include these elements in the copy, use the “t” option in place of the “o” option. When used as a graph procedure, this method is equivalent to the one described above for an existing graph, so that: graph10.template(t) customgraph graph10.bar(t = customgraph) produce the same results. Arranging multiple graphs When you create a multiple graph, EViews automatically arranges the graphs within the graph window. (See “Creating a Graph” on page 33 for information on how to create a mul- tiple graph.) You may use either the “m” option during graph creation or the merge com- mand. To change the placement of the graphs, use the Graph::align command. Specify the num- ber of columns in which to place the graphs and the horizontal and vertical space between graphs, measured in virtual inches. For example: graph graph11.merge graph01 graph02 graph03 graph11.align(2, 1, 1.5) creates a multiple graph GRAPH11 of the graphs GRAPH01, GRAPH02, and GRAPH03. By default, the graphs are stacked in one column. The second command realigns the graphs in two columns, with 1 virtual inch between the graphs horizontally and 1.5 virtual inches between the graphs vertically. Modifying Boxplots The appearance of boxplots can be customized using many of the commands described above. A few special cases and additional commands are described below.
- 60. Customizing a Graph—53 Customizing lines and symbols As with other graph types, the setelem command can be used with boxplots to modify line and symbol attributes, assign the boxes to an axis, and use preset and default settings. To use the Graph::setelem command with boxplots, use a box element keyword after the command. For example: boxgraph01.setelem(mean) symbol(circle) changes the means in the boxplot BOXGRAPH01 to circles. Note that all boxes within a sin- gle graph have the same attributes, and changes to appearance are applied to all boxes. For instance: boxgraph01.setelem(box) lcolor(orange) lpat(dash1) lwidth(2) plots all boxes in BOXGRAPH01 with an orange dashed line of width 2 points. Also note that when shaded confidence intervals are used, a lightened version of the box color will be used for the shading. In this way, the above command also changes the confidence interval shad- ing to a light orange. Each element in a boxplot is represented by either a line or symbol. EViews will warn you if you attempt to modify an inappropriate option (e.g., modifying the symbol of the box). Assigning boxes to an axis The setelem command may also be used to assign the boxes to another axis: boxgraph01.setelem axis(right) Note that since all boxes are assigned to the same axis, the index argument specifying a graph element is not necessary. Using preset line colors During general graph creation, lines and fills take on the characteristics of the user-defined presets. When a boxplot is created, the first user-defined line color is applied to the boxes, whiskers, and staples. Similarly, when you use the preset or default keywords of the setelem command with a boxplot, the line color of the preset is applied to the boxes, whis- kers, and staples. (See “Using preset lines and fills” on page 42 for a description of presets.) The preset and default methods work just as they do for other graph types, although only the line color is applied to the graph. For example: boxgraph01.setelem default(3) applies the line color of the third user-defined line preset to the boxes, whiskers, and staples of BOXGRAPH01. Note again that setelem does not require an argument specifying an index, since the selected preset will apply to all boxes.
- 61. 54—Chapter 2. Working with Graphs There are a number of setelem arguments that do not apply to boxplots. The fillcolor, fillgray, and fillhatch option keywords are not available, as there are no custom areas to be filled. The legend keyword is also not applicable, as boxplots use axis text labels in place of a legend. Hiding boxplot elements In addition to the setelem command, boxplots provide a Graph::setbpelem command for use in enabling or disabling specific box elements. Any element of the boxplot can be hidden, except the box itself. Use the command with a list of box elements to show or hide. For example: boxgraph01.setbpelem -mean far hides the means and confirms that the far outliers are shown in BOXGRAPH01. Modifying box width and confidence intervals The width of the individual boxes in a boxplot can be drawn in three ways: fixed width over all boxes, proportional to the sample size, or proportional to the square root of the sample size. To specify one of these methods, use the setbpelem command with the width key- word, and one of the supported types (fixed, rootn, n). For example: boxgraph01.setbpelem width(rootn) draws the boxes in BOXGRAPH01 with widths proportional to the square root of their sam- ple size. There are three methods for displaying the confidence intervals in boxplots. They may be notched, shaded, or not drawn at all, which you may specify using one of the supported keywords (notch, shade, none). For example: boxgraph01.setbpelem ci(notch) draws the confidence intervals in BOXGRAPH01 as notches. Labeling Graphs As with all EViews objects, graphs have a label view to display and edit information such as the graph name, last modified date, and remarks. To modify or view the label information, use the Graph::label command: graph12.label(r) Data from CPS 1988 March File This command shows the label view, and the “r” option appends the text “Data from CPS 1988 March File” to the remarks field of GRAPH12. To return to the graph view, use the graph keyword: graph12.graph
- 62. Exporting Graphs to Files—55 All changes made in label view will be saved when the graph is saved. Printing Graphs A graph may be printed using the print (p. 454) command. For example: print graph11 graph12 prints GRAPH11 and GRAPH12 on a single page. In addition, many graph commands and graph views of objects include a print option. For example, you can create and simultaneously print a line graph GRA1 of SER1 using the “p” option: graph gra1.line(p) ser1 You should check the individual commands for availability of this option. Exporting Graphs to Files You may use the Graph::save proc of a graph object to save the graph as a Windows meta- file (.wmf), Enhanced Windows metafile (.emf), PostScript file (.eps), bitmap (.bmp), Graphics Interchange Format (.gif), Joint Photographics Exchange Group (.jpg), or Portable Network Graphics (.png) file. You must specify a file name and file type, and may also provide the file height, width, units of measurement, and color use. PostScript files also allow you to save the graph with or without a bounding box and to specify portrait or landscape orientation. For instance: graph11.save(t=postscript, u=cm, w=12, -box) MyGraph1 saves GRAPH11 in the default directory as a PostScript file “MyGraph1.EPS”, with a width of 12 cm and no bounding box. The height is determined by holding the aspect ratio of the graph constant. Similarly: graph11.save(t=emf, u=pts, w=300, h=300, -c) c:dataMyGraph2 saves GRAPH11 as an Enhanced Windows metafile “Mygraph2.EMF”. The graph is saved in black and white, and scaled to points. graph11.save(t=png, u=in, w=5, d=300) MyGraph3 saves GRAPH11 in the default directory as a PNG file “Mygra3.PNG”. The image will be 5 inches wide at 300 dpi. graph11.save(t=gif, u=pixels, w=500) MyGraph4 saves GRAPH11 in a 500 pixel wide GIF file, “Mygraph4.GIF”. 300 300
- 63. 56—Chapter 2. Working with Graphs Graph Summary See “Graph” on page 230 of the Object Reference for a full listing of procs that may be used to customize graph objects, and for a list of the graph type commands. Graph commands are documented in “Graph Creation Command Summary” on page 895 of the Object Reference.
- 64. Chapter 3. Working with Tables and Spreadsheets There are three types of tables in EViews: tabular views, which are tables used in the display of views of other objects, named table objects, and unnamed table objects. The main portion of this discussion focuses on the use of commands to customize the appearance of named table objects. The latter portion of the chapter describes the set of tools that may be used to customize the display characteristics of spreadsheet views of objects (see “Customizing Spreadsheet Views,” beginning on page 67). You may use EViews commands to generate custom tables of formatted output from your programs. A table object is an object made up of rows and columns of cells, each of which can contain either a number or a string, as well as information used to control formatting for display or printing. Chapter 16. “Table and Text Objects,” on page 743 of the Object Reference describes various interactive tools for customizing table views and objects. Creating a Table There are two basic ways to create a table object: by freezing an object view, or by issuing a table declaration. Creating Tables from an Object View You may create a table object from another object, by combining an object view command with the freeze (p. 362) command. Simply follow the freeze keyword with an optional name for the table object, and the tabular view to be frozen. For example, since the com- mand grp6.stats displays the statistics view of the group GRP6, the command freeze(mytab) grp6.stats creates and displays a table object MYTAB containing the contents of the previous view. You should avoid creating unnamed tables when using commands in programs since you will be unable to refer to, or work with the resulting object using commands. If the MYTAB option were omitted in the previous example, EViews would create and display an untitled table object. This table object may be customized interactively, but may not be referred to in programs. You may, of course, assign a name to the table interactively. Once you have created a named table object, you may use the various table object procs to further customize the appearance of your table. See “Customizing Tables,” beginning on page 60.
- 65. 58—Chapter 3. Working with Tables and Spreadsheets Declaring Tables To declare a table, indicate the number of rows and columns and provide a valid name. For example: table(10,20) bestres creates a table with 10 rows and 20 columns named BESTRES. You can change the size of a table by declaring it again. Re-declaring the table to a larger size does not destroy the con- tents of the table; any cells in the new table that existed in the original table will contain their previous values. Tables are automatically resized when you attempt to fill a table cell outside the table’s cur- rent dimensions. This behavior is different from matrix objects which issue an error when an out-of-range element is accessed. Assigning Table Values You may modify the contents of cells in a table using assignment statements. Each cell of the table can be assigned either a string or a numeric value. Assigning Strings To place a string value into a table cell, follow the table name by a cell location (row and column pair in parentheses), then an equal sign and a string expression. For example: table bestres bestres(1,6) = "convergence criterion" %strvar = "lm test" bestres(2,6) = %strvar bestres(2,6) = bestres(2,6) + " with 5 df" creates the table BESTRES and places various string values into cells of the table. Assigning Numbers Numbers can be entered directly into cells, or they can be converted to strings before being placed in the table. Unless there is a good reason to do otherwise, we recommend that numbers be entered directly into table cells. If entered directly, the number will be displayed according to the numerical format set for that cell; if the format is changed, the number will be redisplayed according to the new format. If the number is first converted to a string, the number will be frozen in that form and cannot be reformatted to a different precision. For example:
- 66. Assigning Table Values—59 table tab1 tab1(3,4) = 15.345 tab1(4,2) = 1e-5 !ev = 10 tab1(5,1) = !ev scalar f = 12345.67 tab1(6,2) = f creates the table TAB1 and assigns numbers to various cells. Assignment with Formatting The setcell (p. 469) command is like direct cell assignment in that it allows you to set the contents of a cell, but setcell also allows you to provide a set of simple formatting options for the cell. If you desire even greater control over formatting, or if you wish to alter the for- mat of a cell without altering its contents, you should use the tools outlined in “Customizing Tables,” beginning on page 60. The setcell command takes the following arguments: • the name of the table • the row and the column of the cell • the number or string to be placed in the cell • (optionally) a justification code or a numerical format code, or both The justification codes are: • “c” for centered (default) • “r” for right-justified • “l” for left-justified The numerical format code determines the format with which a number in a cell is dis- played; cells containing strings will be unaffected. The format code can either be a positive integer, in which case it specifies the number of digits to be displayed after the decimal point, or a negative integer, in which case it specifies the total number of characters to be used to display the number. These two cases correspond to the fixed decimal and fixed character fields in the number format dialog. Note that when using a negative format code, one character is always reserved at the start of a number to indicate its sign, and if the number contains a decimal point, that will also be counted as a character. The remaining characters will be used to display digits. If the num- ber is too large or too small to display in the available space, EViews will attempt to use sci-
- 67. 60—Chapter 3. Working with Tables and Spreadsheets entific notation. If there is insufficient space for scientific notation (six characters or less), the cell will contain asterisks to indicate an error. Some examples of using setcell: setcell(tabres,9,11,%label) puts the contents of %LABEL into row 9, column 11 of the table TABRES. setcell(big_tabl,1,1,%info,"c") inserts the contents of %INFO in BIG_TAB1(1,1), and displays the cell with centered justifi- cation. setcell(tab1,5,5,!data) puts the number !DATA into cell (5,5) of table TAB1, with default numerical formatting. setcell(tab1,5,6,!data,4) puts the number !DATA into TAB1, with 4 digits to the right of the decimal point. setcell(tab1,3,11,!data,"r",3) puts the number !DATA into TAB1, right-justified, with 3 digits to the right of the decimal point. setcell(tab1,4,2,!data,-7) puts the number in !DATA into TAB1, with 7 characters used for display. Customizing Tables EViews provides considerable control over the appearance of table objects, providing a vari- ety of table procedures allowing you specify row heights and column widths, content for- matting, justification, font face, size, and color, cell background color and borders. Cell merging and annotation are also supported. Column Width and Row Height We begin by noting that if the contents of a cell are wider or taller than the display width or height of the cell, part of the cell contents may not be visible. You may use the Table::setwidth and Table::setheight table procedures to change the dimensions of a column or row of table cells. To change the column widths for a set of columns in a table, use the setwidth keyword fol- lowed by a column range specification in parentheses, and a desired width. The column range should be either a single column number or letter (e.g., “5”, “E”), a colon delimited range of columns (from low to high, e.g., “3:5”, “C:E”), or the keyword “@ALL”. The width unit is computed from representative characters in the default font for the current table (the EViews table default font at the time the table was created), and corresponds
- 68. Customizing Tables—61 roughly to a single character. Width values may be non-integer values with resolution up to 1/10 of a unit. The default width value for columns in an unmodified table is 10. For example, both commands tab1.setwidth(2) 12 tab1.setwidth(B) 12 set the width of column 2 to 12 width units, while the command tab1.setwidth(2:10) 20 sets the widths for columns 2 through 10 to 20 width units. To set all of the column widths, use the “@ALL” keyword. tab1.setwidth(@all) 20 Similarly, you may specify row heights using the setheight keyword, followed by a row specification in parentheses, and a desired row height. Rows are specified either as a single row number (e.g., “5”), as a colon delimited range of rows (from low to high, e.g., “3:5”), or using the keyword “@ALL”. Row heights are given in height unit values, where height units are in character heights. The character height is given by the font-specific sum of the units above and below the baseline and the leading in the default font for the current table. Height values may be non-integer values with resolution up to 1/10 of a height unit. The default row height value is 1. For example, tab1.setheight(2) 1 sets the height of row 2 to match the table default font character height, while tab1.setheight(2) 3.5 increases the row height to 3-1/2 character heights. Similarly, the command: tab1.setheight(2:7) 1.5 sets the heights for rows 2 through 7 to 1-1/2 character heights. tab1.setheight(@all) 2 sets all row heights to twice the default height. Earlier versions of EViews supported the setting of column widths using the setcolwidth command. This command, which is provided for backward compatibility, offers only a sub- set of the capabilities of Table::setwidth.
- 69. 62—Chapter 3. Working with Tables and Spreadsheets Cell Formatting A host of cell characteristics may be set using table procedures. Each procedure is designed to work on individual cells, ranges of cells, or the entire table. Content Formatting Cell content formatting allows you to alter the appearance of the data in a table cell without changing the contents of the cell. Using the table proc Table::setformat, you may, for example, instruct EViews to change the format of a number to scientific or fixed decimal, or to display a date number in a different date format. These changes in display format do not alter the cell values. To format the contents of table cells, simply follow the table name with a period and the setformat proc keyword, followed by a cell range specification in parentheses, and then a valid numeric or date format string. The cell range may be specified in a number of ways, including individual cells, cell rectangles, row or column ranges or the entire table. See Table::setformat for a description of cell range specification and numeric and date for- mat string syntax. For example, to set the format for the fifth column of a matrix to fixed 5-digit precision, you may provide the format specification: tab1.setformat(e) f.5 To set a format for the cell in the third row of the fifth column to scientific notation with 5 digits of precision, specify the individual cell, as in: tab1.setformat(3,e) e.5 tab1.setformat(e3) e.5 To specify the format for a rectangle of cells, specify the upper left and lower right cells in the rectangle. The following commands set cells in the same region to show 3-significant digits, with negative numbers in parentheses: tab1.setformat(2,B,10,D) (g.3) tab1.setformat(r2c2:r10c4) (g.3) tab1.setformat(b2:d10) (g.3) The rectangle of cells is delimited by row 2, column 2, and row 10, column 4. Alternately you may provide a date format for the table cells. The command: tab1.setformat(@all) "dd/MM/YY HH:MI:SS.SSS" will display numeric values in the entire table using formatted date strings containing days followed by months, years, hours, minutes and seconds, to a resolution of thousandths of a second.
- 70. Customizing Tables—63 Note that changing the display format of a cell that contains a string will have no effect unless the cell is later changed to contain a numerical value. Justification and Indentation The cell justification and indentation control the position of the table cell contents within the table cell itself. You may use the Table::setjust proc to position the cell contents in the cell. Simply use the setjust keyword, followed by a cell range specification in parentheses, and one or more keywords describing a vertical or horizontal position for the cell contents. You may use the keywords auto, left, right, and center to control horizontal positioning, and top, middle, and bottom to control vertical positioning. You may use the auto keyword to spec- ify left justification for string cells and right justification for numeric cells. For example, tab1.setjust(@all) top left sets the justification for all cells in the table to top left, while tab1.setjust(2,B,10,D) center horizontally centers the cell contents in the rectangle from B2 to D10, while leaving the ver- tical justification unchanged. In addition, you may use Table::setindent to specify a left or right indentation from the edge of the cell for cells that are left or right justified, respectively. You should use the setindent keyword followed by a cell range in parentheses, and an indentation unit, spec- ified in 1/5 of a width unit. Indentation is only relevant for non-center justified cells. For example: tab1.setjust(2,B,10,D) left tab1.indent(2,B,10,D) 2 left-justifies, then indents the specified cells by 2/5 of a width unit from the left-hand side of the cell. Alternatively, tab2.setjust(@all) center tab2.indent(@all) 3 will set the indentation for all cells in the table to 3/5 of a width unit, but this will have no effect on the center justified cells. If the cells are later modified to be left or right justified, the indentation will be used. If you subsequently issue the command tab2.indent(@all) right the cells will be indented 3/5 of a width unit from the right-hand edges.
- 71. 64—Chapter 3. Working with Tables and Spreadsheets Fonts You may specify font face and characteristics, and the font color for table cells using the Table::setfont and Table::settextcolor table procs. The setfont proc should be used to set the font face, size, boldface, italic, strikethrough and underline characteristics for table cells. You should provide a cell range specification, and one or more font arguments corresponding to font characteristics that you wish to mod- ify. For example: tab1.setfont(3,B,10,D) "Times New Roman" +u 8pt changes the text in the specified cells to Times New Roman, 8 point, underline. Similarly, tab1.setfont(4,B) -b +i -s adds the italic to and removes boldface and strikethrough from the B4 cell. To set the color of your text, use settextcolor with a cell range specification and a color specification. Color specifications may be provided using the @RGB settings, or using one of the EViews predefined colors keywords: tab1.settextcolor(f2:g10) @rgb(255, 128, 0) tab1.settextcolor(f2:g10) orange sets the text color for the specified cells to orange. See Table::setfillcolor for a com- plete description of color specifications. Background and Borders You may set the background color for cells using the Table::setfillcolor table proce- dure. Specify the cell range and provide a color specification using @RGB settings or one of the predefined color keywords. The commands: tab1.setfillcolor(R2C3:R3C6) ltgray tab1.setfillcolor(2,C,3,F) @rgb(192, 192, 192) both set the background color of the specified cells to light gray. The Table::setlines table proc may be used to draw borders or lines around specified table cells. If a single cell is specified, you may draw borders around the cell or a double line through the center of the cell. If multiple columns or rows is selected, you may, in addition, add borders between cells. Follow the name of the table object with a period, the setlines keyword, a cell range spec- ification, and one or more line arguments describing the lines and borders you wish to draw. For example: tab1.setlines(b2:d6) +a -h -v
- 72. Customizing Tables—65 first adds all borders (“a”) to the cells in the rectangle defined by B2 and D6, then removes the inner horizontal (“h”), and inner vertical (“v”) borders. The command tab1.setlines(2,b) +o adds borders to the outside (“o”), all four borders, of the B2 cell. You may also use the setlines command to place double horizontal separator lines in the table. Enter the setlines keyword, followed by the name of the table, and a row number, both in parentheses. For example, bestres.setlines(8) +d places a separator line in the eighth row of the table BESTRES. The command: bestres.setlines(8) -d removes the double separator lines from all of the cells in the eighth row of the table. Cell Annotation and Merging Each cell in a table object is capable of containing a comment. Cell comments contain text that is hidden until the mouse cursor is placed over the cell containing the comment. Com- ments are useful for adding notes to a table without changing the appearance of the table. To add a comment with the Table::comment table proc, follow the name of the table object with a period, a single cell identifier (in parentheses), and the comment text enclosed in double quotes. If no comment text is provided, a previously defined comment will be removed. To add a comment “hello world” to the cell in the second row, fourth column, you may use the command: tab1.comment(d2) "hello world" To remove the comment simply repeat the command, omitting the text: tab1.comment(d2) In addition, EViews permits you to merge cells horizontally in a table object. To merge mul- tiple cells in a row or to un-merge previously merged cells, you should use the Table::setmerge table proc. Enter the name of the table object, a period, followed by a cell range describing the cells in a single row that are to be merged. If the first specified column is less than the last specified column (left specified before right), the cells in the row will be merged left to right, otherwise, the cells will be merged from right to left. The contents of the merged cell will be taken from the first cell in the merged region. If merging from left to right, the leftmost cell contents will be used; if merging from right to left, the rightmost cell contents will be displayed. For example,
- 73. 66—Chapter 3. Working with Tables and Spreadsheets tab1.setmerge(a2:d2) merges the cells in row 2, columns 1 to 4, from left to right, while tab2.setmerge(d2:a2) merges the cells in row 2, columns 2 to 5, from right to left. The cell display will use the left- most cell in the first example, and the rightmost in the second. If you specify a merge involving previously merged cells, EViews will unmerge all cells within the specified range. We may then unmerge cells by issuing the Table::setmerge command using any of the previously merged cells. The command: tab2.setmerge(r2c4) unmerges the previously merged cells. Labeling Tables Tables have a label view to display and edit information such as the graph name, last modi- fied date, and remarks. To modify or view the label information, use the Table::label command: table11.label(r) Results from GMM estimation This command shows the label view, and the “r” option appends the text “Results from GMM estimation” to the remarks field of TABLE11. To return to the basic table view, use the table keyword: table11.table All changes made in label view will be saved with the table. Printing Tables To print a table, use the print (p. 454) command, followed by the table object name. For example: print table11 The print destination is taken from the EViews global print settings. Exporting Tables to Files You may use the table Table::save procedure to save the table to disk as a CSV (comma separated file), tab-delimited ASCII text, RTF (Rich text format), or HTML file. You must specify a file name and an optional file type, and may also provide options to spec- ify the cells to be saved, text to be written for NA values, and precision with which numbers
- 74. Customizing Spreadsheet Views—67 should be written. RTF and HTML files also allow you to save the table in a different size than the current display. If a file type is not provided, EViews will write a CSV file. For example: tab1.save(t=csv, n="NAN") mytable saves TAB1 in the default directory as a CSV file “Mytable.CSV”, with NA values translated to the text “NAN”. Alternately, the command: tab1.save(r=B2:C10, t=html, s=.5) c:dataMyTab2 saves the specified cells in TAB1 as an HTML file to “Mytab2.HTM” in the directory “c:data”. The table is saved at half of the display size. Customizing Spreadsheet Views Several of the table procs for customizing table display may also be used for customizing spreadsheet views of objects. You may use Series::setformat, Series::setindent, Series::setjust, and Series::setwidth to modify the spreadsheet view of a series. Similar procs are available for other objects with table views (e.g., alpha, group, and matrix objects). Suppose, for example, that you wish to set the format of the spreadsheet view for series SER1. Then the commands: ser1.setformat f.5 ser1.setjust right center ser1.setindent 3 ser1.setwidth 10 ser1.sheet sets the spreadsheet display format for SER1 and then displays the view. Similarly, you may set the characteristics for a matrix object using the commands: mat1.setformat f.6 mat1.setwidth 8 mat1.sheet For group spreadsheet formatting, you must specify a column range specification. For exam- ple: group1.setformat(2) (f.7) group1.setwidth(2) 10 group1.setindent(b) 6 group1.sheet
- 75. 68—Chapter 3. Working with Tables and Spreadsheets set the formats for the second series in the group, then displays the spreadsheet view. group1.setwidth(@all) 10 sets the width for all columns in the group spreadsheet to 10. Note that the group specified formats are used only to display series in the group and are not exported to the underlying series. Thus, if MYSER is the second series in GROUP1, the spreadsheet view of MYSER will use the original series settings, not those specified using the group procs. Table Summary See “Table,” on page 770 of the Object Reference for a full listing of formatting procs that may be used with table objects.
- 76. Chapter 4. Working with Spools The EViews spool object allows you to create sets of output comprised of tables, graphs, text, and other spool objects. Spools allow you to organize EViews results, allowing you to generate a log of output for a project, or perhaps to collect output for a presentation. The following discussion focuses on command methods for working with a spool object. A general description of the spool object, featuring a discussion of interactive approaches to working with your spool, may be found in Chapter 17. “Spool Objects,” on page 755 of User’s Guide I. Creating a Spool There are two methods you may use to create a spool. You may declare a spool using the spool command, or you may print an object to a new spool. To declare an empty spool, use the keyword spool followed by a name for the new spool: spool myNewSpool creates a new, empty spool object MYNEWSPOOL. A new spool may also be created by printing from an object to a non-existent spool. To print to a spool you must redirect the output of print jobs to the spool using the output com- mand. For example, the command: output(s) myNewSpool instructs EViews to send all subsequent print jobs to the MYNEWSPOOL spool (see output (p. 428)). Once you redirect your output, you may create a spool using the print command or the “p” option of an object view or procedure. tab1.print creates the spool object MYNEWSPOOL and appends a copy of TAB1. Alternately, eq1.output(p) appends the EQ1 equation output to the newly created spool object. To turn off redirection, simply issue the command output off
- 77. 70—Chapter 4. Working with Spools Working with a Spool Spool objects provide easy-to-use tools for working with the objects in the spool. Among other things, you may manage (add, delete, extract, rearrange, hide) or customize (resize, space and indent, title and comment, and edit) the spool and the individual objects in a spool. Adding Objects You may add objects to a spool by printing to the spool, or by using the Spool::append and Spool::insert procs. Printing to a Spool Earlier, we saw how one may redirect subsequent print jobs to the spool object using the output (p. 428) command to change the default print destination. Once redirection is in place, simply use the print command or the “p” option to send view or procedure output to the spool. The following command lines: output(s) myOldSpool ser01.line(p) grp01.scat(p) eq1.wald(p) c(1)=c(2) redirect output to the existing spool object MYOLDSPOOL, then adds a line graph of SER01, a scatterplot of the series in GRP01, and the table output of a Wald test for equation EQ1 to the spool, in that order. Note that the three output objects in the spool will be named UNTITLED01, UNTITLED02, and UNTITLD03. To turn off redirection, simply issue the command: output off Appending and Inserting You may use the Spool::append procedure to add output objects to the end of an existing spool object. You may insert any valid EViews object view into a spool. For example, spool01.append ser01.line appends a line graph of SER01 to the end of SPOOL01. The name of the object in the spool will be the next available name beginning with “UNTI- TLED”. For example, if two objects have already been appended to SPOOL01, named UNTI- TLED01 and UNTITLED02, then the line graph of SER01 will be named UNTITLED03. You may append multiple EViews objects using a single append command:
- 78. Working with a Spool—71 spool03.append ser02.line ser03 appends a line graph of SER02 and the default spreadsheet view of SER03 to the end of SPOOL03. The Spool::insert proc offers additional control over the location of the added object by allowing you to specifying an integer position for the inserted object. If a position is not specified or the specified position is greater than the number of objects already in the spool, the object will be appended to the end. The command: spool01.insert(loc=3) series01 inserts the default spreadsheet view of SERIES01 into SPOOL01 at position three. All existing objects in the spool from position three and higher are pushed down in the list to accommo- date the new object. You may include more than one object view using a single insert command: spool01.insert(loc=5) "eq1.wald c(1)=c(2)" series01.uroot inserts both the results for a Wald test on EQ1, and the results for a unit root test for SERIES01 into the spool in the fifth and sixth positions. Existing objects from the fifth posi- tion onward will be moved down to the seventh position so that they follow the unit root table. Note that since the Wald test command contains spaces, we require the use of double quotes to delimit the expression. Alternately, insert accepts an object name for the location and an optional offset keyword. The command: spool01.insert(loc=obj3) mycity.line adds the line graph view of MYCITY to SPOOL01, placing it before OBJ3. You may modify the default positioning by adding the “offset=after” option, spool01.insert(loc=obj3, offset=after) mycity.line so that the line graph is inserted after OBJ3. You may use insert or append to add spool objects to a spool. Suppose that we have the spool objects SPOOL01 and STATESPOOL. Then spool01.insert statespool adds STATESPOOL to the end of SPOOL01. Subsequent insert commands may be used to place objects before, after, or inside of the spool object. The commands spool01.insert(loc=state) mycity.line spool01.insert(loc=state, offset=after) mytown.hist
- 79. 72—Chapter 4. Working with Spools inserts a line graph view of MYCITY before, and the histogram view of MYTOWN after the STATE spool. You may also use the “offset=” option to instruct EViews to place the new output object inside of an embedded spool: spool01.insert(loc=state, offset=first) mycity.boxplot spool01.insert(loc=state, offset=last) mystate.stats places a boxplot view of MYCITY and a descriptive statistics view of MYSTATE inside of the STATE spool object. The boxplot view is inserted at the beginning of STATE, while the descriptive statistics view is appended to the end of STATE. Objects within a embedded spool should be referred to using the full path description. For example, suppose we have a spool object COUNTY which we wish to add to the end of the previously embedded spool STATE. Then, spool01.insert(loc=state, offset=last) county inserts COUNTY as the last member of the spool STATE, and: spool01.insert(loc=state/county, offset=first) mycity.bar inserts a bar graph of MYCITY into the first position of the COUNTY spool. Naming Objects The default name of an object when it is inserted into a spool is UNTITLED followed by the next available number (e.g. UNTITLED03). When using the Spool::append or the Spool::insert procs may use the “name=” option to specify a name. Alternately, you may use the Spool::name command to change the name of an object in the spool. For example, spool01.name untitled03 losangeles renames the UNTITLED03 object to LOSANGELES. Note that names are not case-sensitive, and that they must follow EViews’ standard naming conventions for objects. Names must also uniquely identify objects in the spool. To rename an object contained in an embedded spool, you should provide the full path description of the object. For example, the command: spool01.name untitled01/untitled02 newyork renames the object UNTITLED02 which is contained in the spool UNITITLED01 to NEWYORK. Object Displaynames The Spool::displayname proc may also be used to alter the display name of an object. The default display name of an object is simply the uppercase version of the object name. Display names, which are case-sensitive, not restricted to be valid object names, and need
- 80. Working with a Spool—73 not be unique, allow you to provide a more descriptive label in the tree pane view when dis- playing object names. For example, spool01.displayname untitled03 "Los Angeles" sets the display name for UNTITLED03 object to the text “Los Angeles”. Note that since the desired display name has spaces, we have enclosed the text in double-quotes. Similarly, spool01.displayname untitled01/untitled02 "New York" sets the display name for UNTITLED02 in the spool UNITITLED01 to “New York”. Object Comments The Spool::displayname may be used to assign a comment to an object in the spool. Set- ting a comment for an object is similar to setting the display name. Comments can be multi- line; you may use “n” to indicate the start of a new line in a comment. Spool01.comment untitled01 "The state population of Alabama as foundnfrom http://www.census.gov/popest/states/NST-ann- est.html." assigns the following comment to object UNTITLED01: “The state population of Alabama as found from http://www.census.gov/popest/states/NST-ann-est.html.” Removing Objects Use the Spool::remove proc to delete objects from a spool. Follow the remove keyword with names of the objects to be deleted. The unique object name should be used; the display name cannot be used as a valid argument for the remove command. spool01.remove untitled02 untitled01 untitled03 removes the three objects UNTITLED01, UNTITLED02, UNTITLED03 from SPOOL01. Note that the order at which objects are specified is not important. Extracting Objects Objects within a spool are not confined to spools forever; they may be extracted to other spools using Spool::extract. An independent copy of the specified object will be made. Note that only one object may be extracted at a time. For instance, referring to our example above, where we have a STATE spool containing a COUNTY spool, spool01.extract state/county creates an untitled spool containing the objects in the COUNTY spool.
- 81. 74—Chapter 4. Working with Spools Similarly: spool01.extract(mycounty) state/county Customizing the Spool Titles and Comments Each object in a spool has both an object name and a display name. By default, the object name is shown. The object name is not case sensitive, while the display name can be multi- ple words and is case sensitive. Setting a comment for an object is similar to setting the display name. Comments can be multiline; you may use “n” to indicate the start of a new line in a comment. Spool01.comment untitled01 "The state population of Alabama as foundnfrom http://www.census.gov/popest/states/NST-ann- est.html." assigns the following comment to object UNTITLED01: “The state population of Alabama as found from http://www.census.gov/popest/states/NST-ann-est.html.” Customizing the Appearance General properties of a spool may be modified using the Spool::options proc. These properties include the display of the object tree, borders, titles, comments, and the use of the object name or display name. To change these settings, use the options keyword followed by the characteristic you wish to change. To turn off the tree and display titles, displaynames and comments for SPOOL01: spool01.options -tree titles displaynames comments creates a spool with the same objects and names it MYCOUNTY. Printing the Spool Printing a entire spool object is the same as printing any other object in EViews, simply use the print (p. 454) command followed by the name of the spool: print spool01 prints all of SPOOL01. To print an object stored in SPOOL01, us the Spool::print proc and specify the name of the object within the spool that you wish to print. For example, spool01.print state/county
- 82. Spool Summary—75 prints the COUNTY object, which is located in the STATE spool in SPOOL01. The Spool::print proc also allows you to print multiple objects in the spool. spool01.print state county prints both the STATE and COUNTY objects individually. When printing from the command window, the Print Options dialog will be displayed for each object specified, allowing you to modify printer settings. When printing from a pro- gram, the current printer settings will be used. To modify the current printer settings, you may use File/Print Setup to set the global print defaults (“Print Setup,” on page 833 of User’s Guide I). Spool Summary See “Spool,” on page 662 of the Object Reference for a full listing of procedures that may be used with spool objects.
- 83. 76—Chapter 4. Working with Spools
- 84. Chapter 5. Strings and Dates Strings An alphanumeric string is a set of characters containing alphabetic (“alpha”) and numeric characters, and in some cases symbols, found on a standard keyboard. Strings in EViews may include spaces and dashes, as well as single or double quote characters. Note also that EViews does not support unicode characters. Strings are used in EViews in a variety of places. “Using Strings in EViews” on page 91 offers a brief overview. When entering alphanumeric values into EViews, you generally should enclose your characters in double quotes. The following are all examples of valid string input: "John Q. Public" "Ax$23!*jFg5" "000-00-0000" "(949)555-5555" "11/27/2002" "3.14159" You should use the double quote character as an escape character for double quotes in a string. Simply enter two double quote characters to include the single double quote in the string: "A double quote is given by entering two "" characters." Bear in mind that strings are simply sequences of characters with no special interpreta- tion. The string values “3.14159” and “11/27/2002” might, for example, be used to rep- resent a number and a date, but as strings they have no such intrinsic interpretation. To provide such an interpretation, you must use the EViews tools for translating string values into numeric or date values (see “String Information Functions” on page 82 and “Translating between Date Strings and Date Numbers” on page 100). Lastly, we note that the empty, or null, string (“”) has multiple interpretations in EViews. In settings where we employ strings as a building block for other strings, the null string is interpreted as a blank string with no additional meaning. If, for example, we concatenate two strings, one of which is empty, the resulting string will simply be the non-empty string. In other settings, the null string is interpreted as a missing value. In settings where we use string values as a category, for example when performing categorizations, the null string is interpreted as both a blank string and a missing value. You may then choose
- 85. 78—Chapter 5. Strings and Dates to exclude or not exclude the missing value as a category when computing a tabulation using the string values. This designation of the null string as a missing value is recognized by a variety of views and procedures in EViews and may prove useful. Likewise, when performing string comparisons using blank strings, EViews generally treats the blank string as a missing value. As with numeric comparisons involving missing values, comparisons involving missing values will often generate a missing value. We discuss this behavior in greater detail in our discussion of “String Comparison (with empty strings)” on page 80. String Operators The following operators are supported for strings: (1) concatenation—plus (“+”), and (2) relational—equal to (“=”), not equal to (“<>”), greater than (“>”), greater than or equal to (“>=”), less than (“<“), less than or equal to (“<=”). String Concatenation Operator Given two strings, concatenation creates a new string which contains the first string fol- lowed immediately by the second string. You may concatenate strings in EViews using the concatenation operator, “+”. For example, "John " + "Q." + " Public" "3.14" + "159" returns the strings "John Q. Public" "3.14159" Bear in mind that string concatenation is a simple operation that does not involve interpreta- tion of strings as numbers or dates. Note in particular that the latter entry yields the concat- enated string, “3.14159”, not the sum of the two numeric values, “162.14”. To obtain numeric results, you will first have to convert your strings into a number (see “String Infor- mation Functions” on page 82). Lastly, we note that when concatenating strings, the empty string is interpreted as a blank string, not as a missing value. Thus, the expression "Mary " + "" + "Smith" yields "Mary Smith" since the middle string is interpreted as a blank.
- 86. Strings—79 String Relational Operators The relational operators return a 1 if the comparison is true, and 0 if the comparison is false. In some cases, relational comparisons involving null strings will return a NA. String Ordering To determine the ordering of strings, EViews employs the region-specific collation order as supplied by the Windows operating system using the user’s regional settings. Central to the tasks of sorting or alphabetizing, the collation order is the culturally influenced order of characters in a particular language. While we cannot possibly describe all of the region-specific collation order rules, we note a few basic concepts. First, all punctuation marks and other non alphanumeric characters, except for the hyphen and the apostrophe precede the alphanumeric symbols. The apostro- phe and hyphen characters are treated distinctly, so that “were” and “we’re” remain close in a sorted list. Second, the collation order is case specific, so that the character “a” precedes “A”. In addition, similar characters are kept close so that strings beginning with “a” are fol- lowed by strings beginning with “A”, ahead of strings beginning with “b” and “B”. Typically, we determine the order of two strings by evaluating strings character-by-character, comparing pairs of corresponding characters in turn, until we find the first pair for which the strings differ. If, using the collation order, we determine the first character precedes the second character, we say that the first string is less than the second string and the second string is greater than the first. Two strings are said to be equal if they have the same number of identical characters. If the two strings are identical in every character, but one of them is shorter than the other, then a comparison will indicate that the longer string is greater. A corollary of this statement is that the null string is less than or equal to all other strings. The multi-character elements that arise in many languages are treated as single characters for purposes of comparison, and ordered using region-specific rules. For example, the “CH” and “LL” in Traditional Spanish are treated as unique characters that come between “C” and “L” and “M”, respectively. String Comparison (with non-empty strings) Having defined the notion of string ordering, we may readily describe the behavior of the relational operators for non-empty (non-missing) strings. The “=” (equal), “>=” (greater than or equal), and “<=” (less than or equal), “<>” (not equal), “>” (greater than), and “<” (less than) comparison operators return a 1 or a 0, depending on the result of the string comparison. To illustrate, the following (non region-specific) comparisons return the value 1, "abc" = "abc" "abc" <> "def"
- 87. 80—Chapter 5. Strings and Dates "abc" <= "def" "abc" < "abcdefg" "ABc" > "ABC" "abc def" > "abc 1ef" while the following return a 0, "AbC" = "abc" "abc" <> "abc" "aBc" >= "aB1" "aBC" <= "a123" "abc" >= "abcdefg" To compare portions of strings, you may use the functions @left, @right, and @mid to extract the relevant part of the string (see “String Manipulation Functions” on page 84). The relational comparisons, @left("abcdef", 3) = "abc" @right("abcdef", 3) = "def" @mid("abcdef", 2, 2) = "bc" all return 1. In normal settings, EViews will employ case-sensitive comparisons (see “Case-Sensitive String Comparison” on page 171 for settings that enable caseless element comparisons in programs). To perform a caseless comparison, you should convert the expressions to all uppercase, or all lowercase using the @upper, or @lower functions. The comparisons, @upper("abc") = @upper("aBC") @lower("ABC" = @lower("aBc") both return 1. To ignore leading and trailing spaces, you should use the @ltrim, @rtrim, and @trim func- tions remove the spaces prior to using the operator. The relational comparisons, @ltrim(" abc") = "abc" @ltrim(" abc") = @rtrim("abc ") @trim(" abc ") = "abc" all return 1. String Comparison (with empty strings) Generally speaking, the relational operators treat the empty string as a missing value and return the numeric missing value NA when applied to such a string. Suppose, for example that an observation in the alpha series X contains the string “Apple”, and the corresponding observation in the alpha series Y contains a blank string. All comparisons (“X=Y”, “X>Y”,
- 88. Strings—81 “X>=Y”, “X<Y”, “X<=Y”, and “X<>Y”) will generate an NA for that observation since the Y value is treated as a missing value. Note that this behavior differs from EViews 4 and earlier in which empty strings were treated as ordinary blank strings and not as a missing value. In these versions of EViews, the comparison operators always returned a 0 or a 1. The change in behavior, while regrettable, was necessary to support the use of string missing values. It is still possible to perform comparisons using the previous behavior. One approach is to use the special functions @eqna and @neqna for equality and strict inequality comparisons without propagating NAs (see “String Information Functions” on page 82). For example, you may use the expressions @eqna(x, y) @neqna(x, y) so that blanks in string X or Y are treated as ordinary string values. Using these two func- tions, the observation where X contains “Apple” and Y contains the “” will evaluate to 0 and 1, respectively instead of NA. Similarly, if you specify a relational expression involving a literal blank string, EViews will perform the test treating empty strings as ordinary string values. If, for example, you test x = "" or x < "" all of the string values in X will be tested against the string literal “”. You should contrast this behavior with the behavior for the non-literal tests “X=Y” and “X<Y” where blank values of X or Y result in an NA comparison. Lastly, EViews provides a function for the strict purpose of testing whether a string value is an empty string. The @isempty function tests whether a string is empty. The relational equality test against the blank string literal “” is equivalent to this function. String Lists A string list is an ordinary string that is interpreted as a space delimited list of string ele- ments. For example, the string "Here I stand" may be interpreted as containing three elements, the words “Here”, “I” and “stand”. Double quotes may be used to include multiword elements in a list. Bearing in mind that the quote is used as an escape character for including quotes in strings, the list """Chicken Marsala"" ""Beef Stew"" Hamburger"
- 89. 82—Chapter 5. Strings and Dates contains three elements, the expressions “Chicken Marsala”, “Beef Stew”, and “Hamburger”. Notice how the escaped double quotes are used to group words into single list elements. Interpreting a string as a list of elements allows us to make use of functions which operate on each element in the string, rather than on each character. These methods can be useful for string manipulation and pattern searching. For example, we may find the intersection, union, or cross of two string lists. Additionally, we may manipulate the elements of a string list and find the elements that match or do not match a given pattern. For example, the string list function @wkeep("ABC ABCC AABC", "?B*") uses the pattern “?B*” to filter the string list “ABC ABCC AABC”. Elements with a single character, followed by the character “B”, then followed by any number of other characters are kept, returning: “ABC ABCC”. String Functions EViews provides a number of functions that may either be used with strings, or return string values. Functions that treat a string as a string list begin with a “w”. Some string functions have cor- responding list functions with the same name, preceded by a “w”. For instance, @left returns the leftmost characters of a string, while @wleft returns the leftmost elements of a string list. String Information Functions The following is a brief summary of the basic functions that take strings as an argument and return a number. (Chapter 17. “String and Date Function Reference,” on page 611 offers a more detailed description.) • @length(str): returns an integer value for the length of the string str. @length("I did not do it") returns the value 15. A shortened keyword form of this function, @len, is also supported. • @wcount(str_list): returns an integer value for the number of elements in the string list str_list. @wcount("I did not do it") returns the value 5. • @instr(str1, str2[, int]): finds the starting position of the target string str2 in the base string str1. By default, the function returns the location of the first occurrence of str2 in str1. You may provide an optional integer int to specify the occurrence. If the requested occurrence of the target string is not found, @instr will return a 0.
- 90. Strings—83 The returned integer is often used in conjunction with @mid to extract a portion of the original string. @instr("1.23415", "34") returns the value 4, since the substring “34” appears beginning in the fourth character of the base string, so @mid("1.23415", @instr("1.23415", "34")) returns “3415”. • @wfind(str_list, str_cmp): looks for the string str_cmp in the string list str_list, and returns the element position in the list or 0 if the string is not in the list. @wfind("I did it", "did") returns the value 2. The @wfindnc function performs the same operation, but the comparison is not case- sensitive. • @isempty(str): tests for whether str is a blank string, returning a 1 if str is a null string, and 0 otherwise. @isempty("1.23415") returns a 0, while @isempty("") returns the value 1. • @eqna(str1, str2): tests for equality of str1 and str2, treating null strings as ordinary blank strings, and not as missing values. Strings which test as equal return a 1, and 0 otherwise. For example, @eqna("abc", "abc") returns a 1, while @eqna("", "def") returns a 0. • @neqna(str1, str2): tests for inequality of str1 and str2, treating null strings as ordi- nary blank strings, and not as missing values. Strings which test as not equal return a 1, and 0 otherwise. @neqna("abc", "abc") returns a 0, @neqna("", "def") returns a 1. • @val(str[, fmt]): converts the string representation of a number, str, into a numeric value. If the string has any non-digit characters, the returned value is an NA. You may
- 91. 84—Chapter 5. Strings and Dates provide an optional numeric format string fmt. See “String Conversion Functions” on page 88 and @val (p. 638) for details. • @dateval(str[, fmt]): converts the string representation of a date string, str, into a date number using the optional format string fmt. See “String Conversion Functions” on page 88 and @dateval (p. 618) for details. • @dtoo(str): (Date TO Obs) converts the string representation of a date, str, into an observation value for the active workfile. Returns the scalar offset from the beginning of the workfile associated with the observation given by the date string. The string must be a valid EViews date. create d 2/1/90 12/31/95 %date = "1/1/93" !t = @dtoo(%date) returns the value !T=762. Note that @dtoo will generate an error if used in a panel structured workfile. String Manipulation Functions The following is a brief summary of the basic functions that take strings as an argument and return a string. • @left(str, int): returns a string containing the int characters at the left end of the string str. If there are fewer than int characters, @left will return the entire string. @left("I did not do it", 5) returns the string “I did”. • @wleft(str_list, int): returns a string containing the int elements at the left end of the string list str_list. If there are fewer than int elements, @wleft will return the entire string list. @wleft("I did not do it", 3) returns the string “I did not”. • @right(str, int): returns a string containing the int characters at the right end of a string. If there are fewer than int characters, @right will return the entire string. @right("I doubt that I did it", 8) returns the string “I did it”. • @wright(str_list, int): returns a string containing the int elements at the right end of a string list. If there are fewer than int elements, @wright will return the entire string. @wright("I doubt that I did it", 3) returns the string “I did it”.
- 92. Strings—85 • @mid(str, int1[, int2]): returns the string consisting of the characters starting from position int1 in the string. By default, @mid returns the remainder of the string, but you may specify the optional integer int2, indicating the number of characters to be returned. @mid("I doubt that I did it", 9, 10) returns “that I did”. @mid("I doubt that I did it", 9) returns the string “that I did it”. • @wmid(str_list, int1[, int2]): returns the string consisting of the elements starting from position int1 in the string. By default, @wmid returns all remaining elements of the string, but you may specify the optional integer int2, indicating the number of ele- ments to be returned. @wmid("I doubt that I did it", 2, 3) returns “doubt you did”. @mid("I doubt that I did it", 4) returns the string “I did it”. • @word(str_list, int): returns the int element of the string list. @word("I doubt that I did it", 2) returns the second element of the string, “doubt”. • @wordq(str_list, int): returns the int element of the string list, while preserving quotes. @wordq("""Chicken Marsala"" ""Beef Stew""", 2) returns the second element of the string, “Beef Stew”. The @word function would return the same elements, but would not include quotation marks in the string. • @insert(str1, str2, int): inserts the string str2 into the base string str1 at the position given by the integer int. @insert("I believe it can be done", "not ", 16) returns “I believe it cannot be done”. • @wkeep(str_list, "pattern_list"): returns the list of elements in str_list that match the string pattern pattern_list. The pattern_list is space delimited, and may be made up of any number of “?” (indicates any single character) or “*” (indicates any number of characters). @wkeep("ABC DEF GHI JKL", "?B? D?? *I") keeps the first three elements of the string list, returning the string “ABC DEF GHI”.
- 93. 86—Chapter 5. Strings and Dates • @wdrop(str_list, "pattern_list"): returns a string list, dropping elements in str_list that match the string pattern pattern_list. The pattern_list is space delimited, and may be made up of any number of “?” (indicates any single character) or “*” (indicates any number of characters). @wdrop("ABC DEF GHI JKL", "?B? D?? *I") drops the first three elements of the string list, returning the string “JKL”. • @replace(str1, str2, str3[, int]): returns the base string str1, with the replacement str3 substituted for the target string str2. By default, all occurrences of str2 will be replaced, but you may provide an optional integer int to specify the number of occur- rences to be replaced. @replace("Do you think that you can do it?", "you", "I") returns the string “Do I think that I can do it?”, while @replace("Do you think that you can do it?", "you", "I", 1) returns “Do I think that you can do it?”. • @wreplace(str_list, “src_pattern”, “replace_pattern”): returns the base string list str_list, with the replacement pattern replace_pattern substituted for the target pattern src_pattern. The pattern lists may be made up of any number of “?” (indicates any sin- gle character) or “*” (indicates any number of characters). @wreplace("ABC AB", "*B*", "*X*") replaces all instances of “B” with “X”, returning the string “AXC AX”. @wreplace("ABC DDBC", "??B?", "??X?") replaces all instances of “B” which have two leading characters and one following character, returning the string “ABC DDXC”. • @ltrim(str): returns the string str with spaces trimmed from the left. @ltrim(" I doubt that I did it. ") returns “I doubt that I did it. ”. Note that the spaces on the right remain. • @rtrim(str): returns the string str with spaces trimmed from the right. @rtrim(" I doubt that I did it. ") returns the string “ I doubt that I did it.”. Note that the spaces on the left remain. • @trim(str): returns the string str with spaces trimmed from the both the left and the right. @trim(" I doubt that I did it. ") returns the string “I doubt that I did it.”. • @upper(str): returns the upper case representation of the string str. @upper("I did not do it")
- 94. Strings—87 returns the string “I DID NOT DO IT”. • @lower(str): returns the lower case representation of the string str. @lower("I did not do it") returns the string “i did not do it”. • @addquotes(str): returns the string str with quotation marks added to the left and right. Given a string S1 that contains the unquoted text: I did not do it, @addquotes(S1) returns the quoted string “I did not do it”. • @stripquotes(str): returns the string str with quotation marks removed from the left and right. Given a string S1 that contains the text: “I did not do it”, @stripquotes(S1) returns the unquoted string: “I did not do it”. • @stripparens(str): returns the string str with parentheses removed from the left and right. Given a string S1 that contains the text: “(I did not do it)”, @stripparens(S1) returns the string: “I did not do it”. • @wintersect(str_list1, str_list2): returns the intersection of str_list1 and str_list2. @wintersect("John and Greg won", "Mark won but Greg lost") returns the string “won Greg”. • @wunion(str_list1, str_list2): returns the union of str_list1 and str_list2. @wunion("ABC DEF", "ABC G H def") returns the string “ABC DEF G H def”. Each new element is added to the string list, skipping elements that have already been added to the list. • @wunique(str_list): returns str_list with duplicate elements removed from the list. @wunique("fr1 fr2 fr1") returns the string “fr1 fr2”. • @wnotin(str_list1, str_list2): returns elements of str_list1 that are not in str_list2. @wnotin("John and Greg won", "and Greg") returns the string “John won”. • @wcross(str_list1, str_list2[, “pattern”]): returns str_list1 crossed with str_list2, according to the string pattern. The default pattern is “??”, which indicates that each element of str_list1 should be crossed individually with each element of str_list2. @wcross("ABC DEF", "1 2 3", "?-?")
- 95. 88—Chapter 5. Strings and Dates returns the string list “ABC-1 ABC-2 ABC-3 DEF-1 DEF-2 DEF-3”, inserting a dash (“-”) between each crossed element as the “?-?” pattern indicates. • @winterleave(str_list1, str_list2[, count1, count2]): Interleaves str_list1 with str_list2, according to the pattern specified by count1 and count2. The default uses counts of one. @winterleave("A B C", "1 2 3") interleaves “A B C” with “1 2 3” to produce the string list “A 1 B 2 C 3”. • @wsort(str_list[,”D”]): Returns sorted elements of str_list. Use the “D” flag to sort in descending order. @wsort("fq8 Fq8 xpr1", "D") sorts the string in descending order: “xpr1 Fq8 fq8”. • @wdelim(str_list, "src_delim", "dest_delim"): returns a string list, replacing every appearance of the src_delim delimiter in str_list with a dest_delim delimiter. Delimit- ers must be single characters. @wdelim("Arizona, California, Washington", ",", "-") identifies the comma as the source delimiter and replaces each comma with a dash, returning the string “Arizona-California-Washington”. String Conversion Functions The following functions convert between numbers or date numbers and strings: • @datestr(date1[, fmt]): converts the date number date1 to a string representation using the optional date format string, fmt. @datestr(730088, "mm/dd/yy") will return “12/1/99”, @datestr(730088, "DD/mm/yyyy") will return “01/12/1999”, and @datestr(730088, "Month dd, yyyy") will return “December 1, 1999”, and @datestr(730088, "w") will produce the string “3”, representing the weekday number for December 1, 1999. See the function reference entry @datestr (p. 618) and “Dates” on page 94 for addi- tional details on date numbers and date format strings. • @dateval(str[, fmt]): converts the string representation of a date string, str, into a date number using the optional format string fmt. @dateval("12/1/1999", "mm/dd/yyyy")
- 96. Strings—89 will return the date number for December 1, 1999 (730088) while @dateval("12/1/1999", "dd/mm/yyyy") will return the date number for January 12, 1999 (729765). See the function reference entry @dateval (p. 618) and “Dates,” beginning on page 94 for discussion of date numbers and format strings. • @str(num[, fmt]): returns a string representation of the number num. You may pro- vide an optional numeric format string fmt. @str(153.4) returns the string “153.4”. To create a string containing 4 significant digits and leading “$” character, use @str(-15.4435, "g$.4") The resulting string is “-$15.44”. The expression @str(-15.4435, "f7..2") converts the numerical value, -15.4435, into a fixed 7 character wide decimal string with 2 digits after the decimal and comma as decimal point. The resulting string is “ -15,44”. Note that there is a leading space in front of the “-” character making the string 7 characters long. The expression @str(-15.4435, "e(..2)") converts the numerical value, -15.4435, into a string written in scientific notation with two digits to the right of the decimal point. The decimal point in the value will be rep- resented using a comma and negative numbers will be enclosed in parenthesis. The resulting string is “(1,54e+01)”. A positive value will not have the parenthesis. @str(15.4435, "p+.1") converts the numeric value, 15.4435, into a percentage where the value is multiplied by 100. Only 1 digit will be included after the decimal and an explicit “+” will always be included for positive numbers. The resulting value after rounding is “+1544.4”. See the function reference entry @str (p. 627) for a detailed description of the conver- sion rules and syntax, along with additional examples. • @val(str[, fmt]): converts the string representation of a number, str, into a numeric value. If the string has any non-digit characters, the returned value is an NA. You may provide an optional numeric format string fmt. @val("1.23415") See the function reference entry @val (p. 638) for a detailed description of the conver- sion rules.
- 97. 90—Chapter 5. Strings and Dates String Vector Functions The following functions either take a string vector as an argument, or return a string vector: @rows(str_vector): returns the number of rows in str_vector. For a string vector SV01 with 10 rows, @rows(sv01) returns the integer 10. • @wsplit(str_list): returns a string vector containing the elements of str_list. If the string list SS01 contains “A B C D E F”, then @wsplit(ss01) returns an untitled svector, placing an element of SS01 in each row. Row one of the svector contains “A”, row two contains “B”, etc. • @wjoin(svector): returns a space delimited list containing the elements of svector. This is the inverse of the @wsplit function. Special Functions that Return Strings EViews provides a special, workfile-based function that uses the structure of the active workfile page and returns a set of string values representing the date identifiers associated with the observations. • @strdate(fmt): returns the set of workfile row dates as strings in an Alpha series, formatted using the date format string fmt. See “Special Date Functions” on page 112 for details. In addition, EViews provides two special functions that return a string representations of the date associated with a specific observation in the workfile, or with the current time. • @otod(int): (Obs TO Date): returns a string representation of the date associated with a single observation (counting from the start of the workfile). Suppose, for example, that we have a quarterly workfile ranging from 1950Q1 to 1990Q4. Then @otod(16) returns the date associated with the 16th observation in the workfile in string form, “1953Q4”. • @otods(int): (Obs TO Date in Sample): returns a string representation of the date associated with a single observation (counting from the start of the sample). Thus @otods(2) will return the date associated with the second observation in the current sample. Note that if int is negative, or is greater than the number of observations in the cur- rent sample, an empty string will be returned.
- 98. Strings—91 • @strnow(fmt): returns a string representation of the current date number (at the moment the function is evaluated) using the date format string, fmt. @strnow("DD/mm/yyyy") returns the date associated with the current time in string form with 2-digit days, months, and 4-digit years separated by a slash, “24/12/2003”. You may also ask EViews to report information about objects in the current workfile or data- base, or a directory on your computer, in a form suitable for list processing: • @wlookup(“pattern_list”[, “object_type_list”]): Returns a string list of all objects in the workfile or database that satisfy the pattern_list and, optionally, the object_type_list. The pattern_list may be made up of any number of “?” (indicates any single charac- ter) or “*” (indicates any number of characters). If a workfile contains a graph object named “GR01” and two series objects named “SR1” and “SER2”, then @wlookup("?R?","series") returns the string “SR1”. • @wdir(directory_str): returns a string list of all files in the directory directory_str. Note that this does not include other directories nested within directory_str. @wdir("C:Documents and Settings") returns a string list containing the names of all files in the “C:Documents and Set- tings” directory. Lastly, all EViews objects have data members which return information about themselves in the form of a string. For example: ser1.@updatetime returns the last update time for the series SER1 eq1.@command returns the full command line form of the estimation command. For lists of the relevant data members see the individual object descriptions in Chapter 1. “Object View and Procedure Reference,” on page 2. Using Strings in EViews Strings in EViews are primarily used in four distinct contexts: string variables, string objects, string vectors, or Alpha series.
- 99. 92—Chapter 5. Strings and Dates String Variables A string variable is a temporary variable used in a program whose value is a string. String variables, which only exist during the time that your EViews program is executing, have names that begin with a “%” symbol. For example, %value = "value in millions of u.s. dollars" %armas = "ar(1) ar(2) ma(1) ma(2)" are string variables declarations that may be used in program files. See “String Variables,” on page 128 for extensive discussion of the role that these variables play in programming. String Objects A string object is an EViews workfile object that holds a string of text: string lunch = "Apple Tuna Cookie" string dinner = """Chicken Marsala"" ""Beef Stew"" Hamburger" creates the string objects LUNCH and DINNER, each containing the corresponding string lit- eral. Note that we have used the double quote character as an escape character for double quotes. Since a string object is an EViews workfile object, we may open and display its views. A string object’s view may be switched between String and Word list views. The String view for DINNER displays the text as a single string, "Chicken Marsala" "Beef Stew" Hamburger while the Word list view breaks up the text by element, "Chicken Marsala" "Beef Stew" Hamburger with each element on a separate line. We emphasize the important distinction that string objects are named objects in the workfile that may be saved with the workfile, while string variables are temporary variables that only exist while an EViews program is running. Thus, string objects have the advantage that they may be used interactively, while string variables may not be used outside of programs. String objects can, however, go out of scope when the active workfile page changes, while string variables are always in scope. In all other respects, strings objects and string variables may be used interchangeably in pro- grams. Either string object or string variables can be passed into subroutines for arguments declared as type string.
- 100. Strings—93 String Vectors An svector, or string vector, is an EViews object that holds a string in each row of the vector. A string vector can be created by specifying the number of rows in the vector and providing a name: svector(3) svec If a length is not specified, a one row svector will be created. An svector can be populated by assigning a string or string literal to each row: svec(1) = "gdp cost total" fills the first row of SVEC with the string “gdp cost total”. To assign the same string to all rows, omit the row number. The command svec = "invalid" will assign the string “invalid” to all rows of SVEC. A multiple row svector may be populated using the @wsplit command, which creates a string vector from a string list. For example, svector svec string st = "gdp cost total" svec = @wsplit(st) creates the string vector SVEC of default length one and a string object ST containing “gdp cost total”. The @wsplit command creates a three element svector from the elements of ST, placing the string “gdp” in the first row of the string vector, the string “cost” in the second row, and the string “total” in the third row, and assigns it to SVEC, which is resized accord- ingly. Similarly, an svector will shrink if assigned to a smaller svector. For example, svector svec3 = @wsplit("First Middle Last") svector(10) svec10 svec10 = svec3 first creates the three row svector SVEC3, then assigns the strings “First”, “Middle”, and “Last” to the first, second, and third rows, respectively. The third line creates a second ten row svector, SVEC10. When SVEC3 is assigned to SVEC10, its values are copied over and rows four through ten are removed from SVEC10. An svector may also be filled by concatenating two strings or svectors. For instance, svector s1 = @wsplit("A B C") svector s2 = @wsplit("1 2 3") svector ssvec = s1 + s2
- 101. 94—Chapter 5. Strings and Dates creates two svectors S1 and S2, each with three rows. S1 contains the characters “A”, “B”, and “C”, while S2 contains “1”, “2”, and “3”. The third command creates the svector SSVEC and fills it with the concatenation of the other two svectors, producing “A1” on the first row, “B2” on the second row, and “C3” on the third row. More generally, any operation that can be performed on a string may be performed on ele- ment of an svector. For example, given an svector whose first element contains the string “Hello World” and whose second element contains “Hi there world”, the element assign- ment statement svector sv(3) = @left(sv(1),5) + " " + @mid(sv(2),4,5) takes the left five characters of the first row (“Hello”), adds a space, concatenates five char- acters from the second row, starting at the fourth character (“there”), and assigns it to the third element of SV. Element three of SV now contains the string “Hello there”. The row count of a string vector may be retrieved using the @rows command: scalar sc = @rows(sv) This is especially useful since svectors are dynamically resized when necessary. Alpha Series EViews has a special series type for holding string data. An alpha series object contains a set of observations on string values. Alpha series should be used when you wish to work with variables that contain alphanumeric data, such as names, addresses, and other text. Alpha series are distinguished from string vectors primarily in that their length is tied to the length of the workfile. See “Alpha Series,” on page 198 for discussion. Dates There are a variety of places in EViews where you may work with calendar dates. For most purposes, users need not concern themselves with the intricacies of working with dates. Simply enter your dates in familiar text notation and EViews will automatically interpret the string for you. Those of you who wish to perform more sophisticated operations with dates will, however, need to understand some basic concepts. In most settings, you may simply use text representations of dates, or date strings. For exam- ple, an EViews sample can be set to include only observations falling between two dates specified using date strings such as “May 11, 1997”, “1/10/1990” or “2001q1”. In these set- tings, EViews understands that you are describing a date and will interpret the string accord- ingly.
- 102. Dates—95 Date information may also be provided in the form of a date number. A date number is a numeric value with special interpretation in EViews as a calendar date. EViews allows you to convert date strings into date numbers which may be manipulated using a variety of tools. These tools allow you to perform standard calendar operations such as finding the number of days or weeks between two dates, the day of the week associated with a given day, or the day and time 36 hours from now. The remainder of this section summarizes the use of dates in EViews. (See Chapter 5. “Strings and Dates,” on page 77 for reference material.) There are several tasks that are cen- tral to working with dates: • Translating between date strings and date numbers. • Translating ordinary numbers into date numbers. • Manipulating date numbers using operators and functions. • Extracting information from date numbers. Before turning to these tasks, we must first provide a bit of background on the characteris- tics of date strings, date numbers, and a special class of strings called date formats, which are sometimes employed when translating between the former. Date Strings Date strings are simply text representations of dates and/or times. Most of the conventional ways of representing days, weeks, months, years, hours, minutes, etc., as text are valid date strings. To be a bit more concrete, the following are valid date strings in EViews: "December 1, 2001" "12/1/2001" "Dec/01/01 12am" "2001-12-01 00:00" "2001qIV” As you can see, EViews is able to handle a wide variety of representations of your dates and times. You may use everything from years represented in 1, 2, and 4-digit Arabic form (“1”, “01”, “99”, “1999”), to month names and abbreviations (“January”, “jan”, “Jan”), to quar- ter designations in Roman numerals (“I” to “IV”), to weekday names and abbreviations (“Monday”, “Mon”), to 12 or 24-hour representations of time (“11:12 pm”, “23:12”). A full list of the recognized date string components is provided in “Date Formats” on page 97. It is worth noting that date string representations may be divided up into those that are unambiguous and those that are ambiguous. Unambiguous date strings have but a single interpretation as a date, while ambiguous date strings may be interpreted in multiple ways.
- 103. 96—Chapter 5. Strings and Dates For example, the following dates may reasonably be deemed unambiguous: "March 3rd, 1950" "1980Q3" "9:52PM" while the following dates are clearly ambiguous: "2/3/4" "1980:2" "02:04" The first date string in the latter set is ambiguous because we cannot tell which of the three fields is the year, which is the month, and which is the day, since different countries of the world employ different orderings. The second string is ambiguous since we cannot deter- mine the period frequency within the year. The “2” in the string could, for example, refer to the second quarter, month, or even semi-annual in the year. The final string is ambiguous since it could be an example of a time of day in “hour:minute” format (2:04 am), or a date in “year:period” notation (i.e., the fourth month of the year 2002) or “period:year” notation (i.e., the second month of 2004). In settings where date input is required, EViews will generally accept date string values without requiring you to provide formatting information. It is here that the importance of the distinction between ambiguous and unambiguous date strings is seen. If the date string is unambiguous, the free-format interpretation of the string as a date will produce identical results in all settings. On the other hand, if the date string is ambiguous, EViews will use the context in which the date is being used to determine the most likely interpretation of the string. You may find that ambiguous date strings are neither interpreted consistently nor as desired. These issues, and methods of getting around the problem of ambiguity, are explored in greater detail in “Translating between Date Strings and Date Numbers” on page 100. Date Numbers Date information is often held in EViews in the form of a date number. A date number is a double precision number corresponding to an instance in time, with the integer portion rep- resenting a specific day, and the decimal fraction representing time during the day. The integer portion of a date number represents the number of days in the Gregorian prolep- tic calendar since Monday, January 1, A.D. 0001 (a “proleptic” calendar is a calendar that is applied to dates both before and after the calendar was historically adopted). The first repre- sentable day, January 1, A.D. 1 has an integer value of 0, while the last representable day, December 31, A.D. 9999, has an integer value of 3652058.
- 104. Dates—97 The fractional portion of the date number represents a fraction of the day, with resolution to the millisecond. The fractional values range from 0 (12 midnight) up to (but not including) 1 (12 midnight). A value of 0.25, for example, corresponds to one-quarter of the day, or 6:00 a.m. It is worth noting that the time of day in an EViews date number is accurate up to a particu- lar millisecond within the day, although it can always be displayed at a lower “precision” (larger unit of time). When date numbers are formatted to lower precisions, they are always rounded down to the requested precision and never rounded up. Thus, when displaying the week or month associated with a date number, EViews always rounds down to the begin- ning of the week or month. Date Formats A date format string (or date format, for short) is a string made up of text expressions that describe how components of a date and time may be encoded in a date string. Date formats are used to provide an explicit description of a date string representation, and may be employed when converting between strings or numbers and date numbers. Before describing date formats in some detail, we consider a simple example. Suppose that we wish to use the date string “5/11/1997” to represent the date May 11, 1997. The date for- mat corresponding to this text representation is "mm/dd/yyyy" which indicates that we have, in order, the following components: a one or two-digit month identifier, a “/” separator, a one or two-digit day identifier, a “/” separator, and a 4-digit year identifier. Alternatively, we might wish to use the string “1997-May-11” to represent the same date. The date format for this string is "yyyy-Month-dd" since we have a four-digit year, followed by the full name of the month (with first letter cap- italized), and the one or two-digit day identifier, all separated by dashes. Similarly, the ISO 8601 representation for 10 seconds past 1:57 p.m. on this date is “1997-05- 11 13:57:10”. The corresponding format is "yyyy-MM-DD HH:mi:ss" Here, we have used the capitalized forms of “MM”, “DD”, and “HH” to ensure that we have the required leading zeros. A full description of the components of a date format is provided below. Some of the more commonly used examples of date formats are listed in the options for the setformat object commands (see, for example, Table::setformat (p. 786) in the Object Reference).
- 105. 98—Chapter 5. Strings and Dates Date Format Components A date format may contain one or more of the following string fragments corresponding to various date components. In most cases, there are various upper and lowercase forms of the format component, corresponding either to the presence or absence of leading zeros, or to the case of the string identifiers. The following format strings are the basic components of a date format: Years Year formats use either two or four digit years, with or without leading zeros. The corre- sponding date format strings are: • “yyyy” or “YYYY”: four digit year without/with leading zeros. • “yy” or “YY”: two digit year without/with leading zeros. • “year” or “YEAR”: synonym for “yyyy” and “YYYY”, respectively. Semi-Annual The semi-annual format corresponds to a single digit representing the period in the year: • “s” or “S”: one digit half-year (1 or 2). Quarters Quarter formats allow for values entered in either standard (Arabic) or Roman numbers: • “q” or “Q”: quarter number, always without leading zeros (1 to 4). • “qr” or “QR”: quarter in Roman numerals following the case of the format string (“i” to “iv” or “I” to “IV”.) Months Month formats may represent two-digit month values with or without leading zeros, three- letter abbreviations for the month, or the full month name. The text identifiers may be all lowercase, all uppercase or “namecase” in which we capitalize the first letter of the month identifier. The corresponding format strings are given by: • “mm” or “MM”: two-digit month without/with leading zeros. • “mon”, “Mon”, or “MON”: three-letter form of month, following the case of the for- mat string(“jan”, “Feb”, “MAR”). • “month”, “Month”, or “MONTH”: full month name, following the case of the format string (“january”, “February”, “MARCH”). Weeks Week of the year formats may be specified with or without leading zeros:
- 106. Dates—99 • “ww” or “WW”: week of year (with first week starting from Jan 1st) without/with leading zeros. Days Day formats correspond to day of the year, business day of the year, day of the month, or day of the week, in various numeric and text representations. • “ddd” or “DDD”: day of year without/with leading zeros. • “bbb” or “BBB”: business day of year without/with leading zeros (only counting Monday-Friday). • “dd” or “DD”: day of month without/with leading zeros. • “day” or “DAY”: day of month with suffix, following the case of the format string (“1st”, “2nd”, “3RD”). • “w” or “W”: weekday number (1-7) where 1 is Monday. • “wdy”, “Wdy”, or “WDY”: three-letter weekday abbreviation, following the case of the format string (“Mon”, “Tue”, “WED”). • “weekday”, “Weekday”, or “WEEKDAY”: full weekday name, following the case of the format string (“monday”, “Tuesday”, “WEDNESDAY”). Time (Hours/Minutes/Seconds) The time formats correspond to hours (in 12 or 24 hour format), minutes, seconds, and frac- tional sections, with or without leading zeros and with or without the AM/PM indicator where appropriate. • “hh” or “HH”: hour in 24-hour format without/with leading zeros. • “hm” or “HM”: hour in 12-hour format without/with leading zeros. • “am” or “AM”: two letter AM/PM indicator for 12-hour format, following the case of the format string. • “a” or “A”: single letter AM/PM indicator for 12-hour format, following the case of the format string. • “mi” or “MI”: minute, always with leading zeros. • “ss.s”, “ss.s”, “ss.ss”, or “ss.sss”: seconds and tenths, hundreths, and thousandths-of- a-second, with leading zeros. The capitalized forms of these formats (“SS”, “SS.S”, ...) yield identical results. Delimiters You may use text to delimit the date format components:
- 107. 100—Chapter 5. Strings and Dates • “f” or “F”: use frequency delimiter taken from the active, regular frequency workfile page. The delimiter corresponds to the letter associated with the current workfile fre- quency (“a”, “m”, “q”, ..., “A”, “M”, “Q”, ...), following the case of the format string, or the colon (“:”), as determined by the Global Options setting (Options/General Options.../Date representation). • “?” : used as “wildcard” single character for skipping a character formats used on date number input. Passed through to the output string. • Other alphabetical characters are errors unless they are enclosed in square brackets e.g. “[Q]”, in which case they are passed through to the output (for example, the “standard-EViews” quarterly format is “YYYY[Q]Q”, where we use a four digit year identifier, followed by a “Q” delimiter/identifier, followed by a single digit for the quarter “1990Q2”). • All other characters (e.g., punctuation) are passed through to the input or output without special interpretation. Translating between Date Strings and Date Numbers There are times when it is convenient to work with date strings, and times when it is easier to work with date numbers. For example, when we are describing or viewing a specific date, it is easier to use a “human readable” date string such as “2002-Mar-20”, “3/20/2002”, or “March 20, 2002 12:23 pm” than the date number 730928.515972. Alternatively, since date strings are merely text representations of dates, working with date numbers is essential when manipulating calendar dates to find elapsed days, months or years, or to find a specific date and time 31 days and 36 hours from now. Accordingly, translating between string representations of dates and date numbers is one of the more important tasks when performing advanced operations with dates in EViews. These translations occur in many places in EViews, ranging from the interpretation of date strings in sample processing, to the spreadsheet display of series containing date numbers, to the import and export of data from foreign sources. In most settings, the translations take place automatically, without user involvement. For example, when you enter a sample command of the form smpl 1990q1 2000q4 EViews automatically converts the date strings into a range of date numbers. Similarly, when you edit a series that contains date numbers, you typically will enter your data in the form of a date string such as "2002-Mar-20"
- 108. Dates—101 which EViews will automatically translate into a date number. In other cases, you will specifically request a translation by using the built-in EViews func- tions @datestr (to convert a date number to a string) and @dateval (to convert a date string to a date number). For example, the easiest way to identify the date 1,000 days after May 1, 2000 is first to con- vert the string value “May 1, 2000” into a date number using @dateval, to manipulate the date number to find the value 1000 days after the original date, and finally to convert the resulting date number back into a string using @datestr. See “Formatted Conversion” on page 105 and “Manipulating Date Numbers” on page 109 for additional details. All translations between dates strings and date numbers involve one of two methods: • First, EViews may perform a free-format conversion in which the date format is inferred from the string values, in some cases other contextual information. • Second, EViews may perform a formatted conversion in which the string representa- tion of the dates is provided explicitly via a date format. For the most part, you should find that free-format conversion is sufficient for most needs. Nevertheless, in some cases the automatic handling of dates by EViews may not produce the desired results. If this occurs, you should either modify any ambiguous date formats, or specify an explicit formatted conversion to generate date numbers as necessary. Free-format Conversion EViews will perform free-format conversions between date strings and numbers whenever: (1) there is an automatic translation between strings and numbers, or (2) when you use one of the translation functions without an explicit date format. When converting from strings to numbers, EViews will produce a date number using the “most likely” interpretation of the date string. For the most part, you need not concern your- self with the details of the conversion, but if you require additional detail on specific topics (e.g., handling of date intervals, the implicit century cutoff for 2-digit years) see “Free-for- mat Conversion Details” on page 114. When converting from date numbers to strings, EViews will use the global default settings to determine the default date format, and will display all significant information in the date number. Converting Unambiguous Date Strings to Numbers The free-format conversion of unambiguous date strings (see “Date Strings” on page 95), to numbers will produce identical results in all settings. The date string: "March 3rd, 1950"
- 109. 102—Chapter 5. Strings and Dates will be interpreted as the third day of the third month of the year A.D. 1950, and will yield the date value 711918.0. Note that the date value is the smallest associated with the given date, corresponding to 12 midnight. Similarly, the date string: "1980Q3" is interpreted as the first instance in the third quarter of 1980. EViews will convert this string into the date number representing the smallest date value in that quarter, 722996.0 (12 mid- night on July 1, 1980). If we specify a time string without a corresponding day, "9:52PM" the day portion of the date is set to 0 (effectively, January 1, A.D. 1), yielding a value of 0.91111111 (see “Incomplete Date Numbers” on page 115) for details. Consider also the following ambiguous date string: "1 May 03" While this entry may appear to be ambiguous since the “03” may reasonably refer to either 1903 or 2003, EViews resolves the ambiguity by assuming that if the two-digit year is greater than or equal to 30, the year is assumed to be from the twentieth century, otherwise the year is assumed to be from the twenty first century (see “Two-digit Years” on page 115 for discus- sion). Consequently free-format conversion of two-digit years will produce consistent results in all settings. Converting Ambiguous Date Strings to Numbers Converting from ambiguous date strings will yield context sensitive results. In cases involv- ing ambiguity, EViews will determine the most likely translation format by examining sur- rounding data or applicable settings for clues as to how the date strings should be interpreted. The following contextual information is used in interpreting ambiguous free-form dates: • For implicit period notation (e.g., “1990:3”) the current workfile frequency is used to determine the period. • Choosing between ambiguous “mm/dd” or “dd/mm” formats is determined by exam- ining the values of related date strings (i.e., those in the same series), user-specified date/time display formats for a series or column of a spreadsheet, or by examining the EViews global setting for date display, (Options/General Options.../Date representa- tion). To fix ideas, we consider a few simple examples of the use of contextual information.
- 110. Dates—103 If you specify an ambiguous sample string, EViews will use the context in which the sample is used, the frequency of the workfile, to determine the relevant period. For example, given the sample statement smpl 90:1 03:3 and a quarterly workfile, the sample will be set from 1990q1 to 2003q3. If the workfile is monthly, the sample will be set from January 1990 to March 2003. Suppose instead that you are editing a series spreadsheet where your date numbers are dis- played as dates strings using a specified format. In this setting, EViews allows you to enter your values as date strings, instead of having to enter the underlying date numbers. In this context, it is natural for EViews to use the current display format as a hint in interpreting ambiguous data. For example, if the current display format is set to “Month dd, YYYY” then an input of “2/3/4” or “@dateval("2/3/4")” will be interpreted as February the 3rd, 2004. On the other hand, if the current display format is set to “YYYY-MM-DD” then the same input will be interpreted as the March the 4th, 2002. In settings where an entire series is provided to an EViews procedure, EViews is able to use all of the values in the series to aid in determining the underlying data format. For example, when an alpha series is provided as a date identifier for restructuring a workfile, EViews will first scan all the values of the series in order to decide on the most likely format of all of the data before converting the string in each element into a date number. If the first observation of the series is an ambiguous “2/3/4” but a later observation is “3/20/95” then the “2/3/4” will be interpreted as the 3rd of February 2004 since that is the only order of year, month and day that is consistent with the “3/20/95” observation.
- 111. 104—Chapter 5. Strings and Dates Conversely, when generating new series values with a genr or series assignment statement, EViews processes observation individually and is therefore unable to obtain contextual information to aid in interpreting ambiguous date strings. In this case, EViews will use the global workfile setting for the Month/Day order in dates to determine the ordering of the days and months in the string. For example, when the expression series dnums = @dateval("2/3/4") is used to generate a series containing date values, EViews will interpret the value as Febru- ary 3, 2004, if the global setting is Month/Day/Year, and March 2, 2004, if the global setting is Day/Month/Year. Converting Date Numbers to Strings EViews provides the @datestr function to translate a date number to a date string. We describe the function in detail in “Formatted Conversion” on page 105, but for now, simply note that @datestr takes an optional argument describing the date format to be used when exporting the string. If the optional argument is not provided, EViews will perform a free- format conversion. In performing the free-format conversion, EViews examines two pieces of information. First, the global default settings for the Month/Day order in dates will be used to determine the ordering of days and months in the string. Next, EViews examines the date values to be translated and looks for relevant time-of-day information. If there is no relevant time-of-day information in the date numbers (e.g., the non-integer por- tions are all zero), EViews writes the string corresponding to the date using either the date format "dd/mm/yyyy" or "mm/dd/yyyy” with preference given to the order favored in the global settings. If there is relevant time-of-day information, EViews will extend the date format accordingly. Thus, if days are favored in the ordering, and relevant hours (but not minutes and seconds) information is present, EViews will use "dd/mm/yyyy hh" while if hours-minutes are present, the format will be "dd/mm/yyyy hh:mi" and so forth.
- 112. Dates—105 Formatted Conversion While the free-format conversions will generally produce the desired results, there may be times when you want to exercise precise control over the conversion. EViews will perform a formatted conversion between date strings and date numbers whenever you use the @dat- eval or @datestr functions with the optional second argument specifying an explicit date format. To convert a date string into a date number using a date format, you should use the @dat- eval function with two arguments. The first argument must be a valid date string, and the second must be the corresponding date format string. If you omit the optional second argu- ment, EViews will perform a free-format conversion. • @dateval(str[, fmt]): takes the string str and evaluates it to a date number using the optional date format string, fmt. A few simple examples will illustrate the wide range of string to date number conversions that are possible using @dateval and a date format. The simplest format strings involve the standard month/day/year date descriptions: @dateval("12/1/1999", "mm/dd/yyyy") will return the date number for December 1, 1999 (730088), @dateval("12/1/1999", "dd/mm/yyyy") returns the date number for January 12, 1999 (729765). Here we have changed the interpre- tation of the date string from “American” to “European” by reversing the order of the parts of the format string. Likewise, we may find the first date value associated with a given period @dateval("1999", "yyyy") returns the value 729754.0 corresponding to 12 midnight on January 1, 1999, the first date value for the year 1999. Conversion of an broad range of date strings is possible by putting together various date for- mat string components. For example, @dateval("January 12, 1999", "Month dd, yyyy") returns the date number for 12 midnight on January 12, 1999 (729765), while @dateval("99 January 12, 9:37 pm", "yy Month dd, hm:mi am") yields the value 729765.900694 corresponding to the same date, but at 9:37 in the evening. In this example, the “hm:mi” corresponds to hours (in a 12 hour format, with no leading 0’s) and minutes, and the “am” indicates that there is an indicator for “am” and “pm”. See “Date Strings” on page 95 and “Date Formats” on page 97 for additional details.
- 113. 106—Chapter 5. Strings and Dates To translate a date number to a date string using a date format, you should use the @dat- estr function with two arguments. The first argument must be a valid date number, and the second must be a date format string describing a string representation of the date. • @datestr(date_val[, fmt]): converts the date number into a string, using the optional date format fmt. If a format is not provided, EViews will use a default method (see “Converting Date Numbers to Strings” on page 104). For example, @datestr(730088,"mm/dd/yy") will return “12/1/99”, @datestr(730088,"DD/mm/yyyy") will return “01/12/1999”, and @datestr(730088,"Month dd, yyyy") will return “December 1, 1999”, and @datestr(730088,"w") will produce the string “3”, representing the weekday number for December 1, 1999. See “Date Numbers” on page 96 and “Date Formats” on page 97 for additional details. Translating Ordinary Numbers into Date Numbers While date information is most commonly held in the form of date strings or date numbers, one will occasionally encounter data in which a date is encoded as a (non-EViews format) numeric value or values. For example, the first quarter of 1991 may be given the numeric representation of 1991.1, or the date “August 15, 2001” may be held in the single number 8152001, or in three numeric values 8, 15, and 2001. The @makedate function is used to translate ordinary numbers into date numbers. It is sim- ilar to @dateval but is designed for cases in which your dates are encoded in one or more numeric values instead of date strings: • @makedate(arg1[, arg2[,arg3]], fmt): takes the numeric values given by the argu- ments arg1, and optionally, arg2, etc. and returns a date number using the required format string, fmt. Only a subset of all date formats are supported by @makedate. If more than one argument is provided, the arguments must be listed from the lowest frequency to the highest, with the first field representing either the year or the hour. The simplest form of @makedate involves converting a single number into a date or a time. The following are the supported formats for converting a single number into a date value: • “yy” or “yyyy”: two or four-digit years. • “yys” or “yyyys”: year*10 + half-year.
- 114. Dates—107 • “yy.s” or “yyyy.s”: year + half-year/10. • “yyq” or “yyyyq”: year*10 + quarter. • “yy.q” or “yyyy.q”: year + quarter/10. • “yymm” or “yyyymm”: year*10 + month. • “yy.mm” or “yyyy.mm”: year + month/10. • “yyddd” or “yyyyddd”: year*1000 + day in year. • “yy.ddd” or “yyyy.ddd”: year + day in year/1000. • “yymmdd” or “yyyymmdd”: year*10000 + month*100 + day in month. • “mmddyy”: month*10000 + day in month*100 + two-digit year. • “mmddyyyy”: month*100000 + day in month*10000 + four-digit year. • “ddmmyy”: day in month*10000 + month*100 + two-digit year. • “ddmmyyyy”: day in month*1000000 + month*10000 + four-digit year. The following formats are supported for converting a single number into intraday values: • “hh”: hour in day (in 24 hour units) • “hhmi”: hour*100 + minute. • “hhmiss”: hour*10000 + minute*100 + seconds. Note that the @makedate format strings are not case sensitive, since the function requires that all non-leading fields must have leading zeros where appropriate. For example, when using the format “YYYYMMDD”, the date March 1, 1992 must be encoded as 19920301, and not 199231, 1992031, or 1992301. Let us consider some specific examples of @makedate conversion of a single number. You may convert a numeric value for the year into a date number using a format string to describe the year. The expressions: @makedate(1999, "yyyy") @makedate(99, "yy") both return the date number 729754.0 corresponding to 12 midnight on January 1, 1999. Similarly, you may convert a numeric value into the number of hours in a day using expres- sions of the form, @makedate(12, "hh") Here, EViews will return the date value 0.5 corresponding to 12 noon on January 1, A.D. 1. While this particular date value is not intrinsically of interest, it may be combined with other date values to obtain the value for a specific hour in a particular day. For example using date arithmetic, we may add the 0.5 to the 729754.0 value (12 midnight, January 1,
- 115. 108—Chapter 5. Strings and Dates 1999) obtained above, yielding the date value for 12 noon on January 1, 1999. We consider these sorts of operations in greater detail in “Manipulating Date Numbers” on page 109. If your number contains “packed” date information, you may interpret the various compo- nents using @makedate with an appropriate format string. For example, @makedate(199003, "yyyymm") @makedate(1990.3, "yyyy.mm") @makedate(1031990, "ddmmyyyy") @makedate(30190, "mmddyy") all return the value 726526.0, representing March 1, 1990. Cases where @makedate is used to convert more than one argument into a date or time are more limited and slightly more complex. The arguments must be listed from the lowest fre- quency to the highest, with the first field representing either the year or the hour, and the remaining fields representing sub-periods. The valid date format strings for the multiple argument @makedate are a subset of the date format strings, with components applied sequentially to the numeric arguments: • “yy s” or “yyyy s”: two or four-digit year and half-year. • “yy q” or “yyyy q”: year and quarter. • “yy mm” or “yyyy mm”: year and month. • “yy ddd” or “yyyy ddd”: year and day in year. • “yy mm dd” or “yyyy mm dd”: year, month, and day in month. Similarly, the valid formats for converting multiple numeric values into a time are: • “hh mi”: hour*100 + minute. • “hh mi ss”: hour*10000 + minutes*100 + seconds. For convenience, the non-space-delimited forms of these format strings are also supported (e.g., “yymm”, and “hhmi”). For example, the expressions, @makedate(97, 12, 3, "yy mm dd") @makedate(1997, 12, 3, "yyyymmdd") will return the value 729360.0 corresponding to midnight on December 3, 1997. You may provide a subset of this information so that @makedate(97, 12, "yymm") returns the value 729358.0 representing the earliest date and time in December of 1997 (12 midnight, December 1, 1997). Likewise,
- 116. Dates—109 @makedate(1997, 37, "yyyy ddd") yields the value 729060.0 (February 6, 1997, the 37th day of the year) and @makedate(14, 25, 10, "hh mi ss") returns the value 0.600810185 corresponding to 2:25:10 pm on January 1, A.D. 1. It is worth pointing out that in the examples above, the numeric arguments are entered from lowest frequency to high, as required. The following example, in which days appear before months and years, is not a legal specification @makedate(7, 10, 98, "dd mm yy") and will generate an error reporting a “Bad date format”. Lastly, we note that limitations on the date formats supported by @makedate imply that in some cases, you are better off working with strings and the @dateval function. In cases, where @makedate does not support a desired conversion, you should consider converting your numbers into strings, performing string concatenation, and then using the richer set of @dateval conversions to obtain the desired date values. Manipulating Date Numbers One of the most important reasons for holding your date information in the form of date numbers is so that you may perform sophisticated calendar operations. Date Operators Since date values are simply double precision numbers, you may perform standard mathe- matical operations using these values. While many operations such as division and multipli- cation do not preserve the notion of a date number, you may find addition and subtraction and relational comparison of date values to be useful tools. If, for example, you add 7 to a valid date number, you get a value corresponding to the same time exactly seven days later. Adding 0.25 adds one-quarter of a day (6 hours) to the current time. Likewise, subtracting 1 gives the previous day, while subtracting 0.5 gives the date value 12 hours earlier. Taking the difference between two date values yields the number of days between the two values. While using the addition and subtraction operators is valid, we strongly encourage you to use the EViews specialized date functions since they allow you to perform arithmetic at var- ious frequencies (other than days), while taking account of irregularities in the calendar (see “Functions for Manipulating Dates” on page 110). Similarly, while you may round a date number down to the nearest integer to obtain the first instance in the day, or you may round down to a given precision to find the first instance in a month, quarter or year, the built-in functions provide a set of simple, yet powerful tools for working with dates.
- 117. 110—Chapter 5. Strings and Dates Note further that all of the relational operators are valid for comparing date numbers. Thus, if two date numbers are equal, the “=”, “>=”, and “<=” relational operators all return a 1, while the “<>”, “>”, and “<” comparison operators return a 0. If two date numbers are not equal, “<>” returns a 1 and “=” returns a 0. If the first date number is less than a second date number, the corresponding first date precedes the second in calendar time. Functions for Manipulating Dates EViews provides several functions for manipulating dates that take date numbers as input and return numeric values that are also date numbers. These functions may be used when you wish to find a new date value associated with a given date number, for example, a date number 3 months before or 37 weeks after a given date and time. The functions described below all take a time unit string as an argument. As the name sug- gests, a time unit string is a character representation for a unit of time, such as a month or a year. The valid time unit string values are: “A” or “Y” (annual), “S” (semi-annual), “Q” (quarters), “MM” (months), “WW” (weeks), “DD” (days), “B” (business days), “HH” (hours), “MI” (minutes), “SS” (seconds). There are three primary functions for manipulating a date number: • @dateadd(date1, offset[, u]): returns the date number given by date1 offset by offset time units as specified by the time unit string u. If no time unit is specified, EViews will use the workfile regular frequency, if available. Suppose that the value of date1 is 730088.0 (midnight, December 1, 1999). Then we can add and subtract 10 days from the date by using the functions @dateadd(730088.0, 10, "dd") @dateadd(730088.0, -10, "dd") which return 730098.0 (December 11, 1999) and (730078.0) (November 21, 1999). Note that these results could have been obtained by taking the original numeric value plus or minus 10. The @dateadd function allows for date offsets specified in various units of time. For example, to add 5 weeks to the existing date, simply specify “W” or “WW” as the time unit string, as in @dateadd(730088.0, 5, "ww") which returns 730123.0 (January 5, 2000). • @datediff(date1, date2[, u]): returns the number of time units between date1 and date2, as specified by the time unit string u. If no time unit is specified, EViews will use the workfile regular frequency, if available. Suppose that date1 is 730088.0 (December 1, 1999) and date2 is 729754.0 (January 1, 1999), then,
- 118. Dates—111 @datediff(730088.0, 729754.0, "dd") returns 334 for the number of days between the two dates. Note that this is result is simply the difference between the two numbers. The @datediff function is more powerful in that it allows us to calculate differences in various units of time. For example, the expressions @datediff(730088.0, 729754.0, "mm") @datediff(730088.0, 729754.0, "ww") return 11 and 47 for the number of months and weeks between the dates. • @datefloor(date1, u[, step]): finds the first possible date number in the given time unit, as in the first possible date value in the current quarter, with an optional step offset. If step is omitted, the frequency will use a step of 1 so that by default, @datefloor will find the beginning of the period defined by the time unit. Suppose that date1 is 730110.5 (12 noon, December 23, 1999). Then the @datefloor values @datefloor(730110.5, "dd") @datefloor(730110.5, "mm") yield 730110.0 (midnight, December 23, 1999) and 730088.0 (midnight, December 1, 1999), since those are the first possible date values in the current day and month. Note that the first value is simply the integer portion of the original date number, but that the latter required more complex analysis of the calendar. Likewise, we can find the start of any corresponding period by using different time units: @datefloor(730098.5, "q") @datefloor(730110.5, "y", 1) returns 730027.0 (midnight, October 1, 1999), and 729754.0 (midnight, January 1, 1999. Notice that since the latter example used an offset value of 1, the result corre- sponds to the first date value for the year 1999, which is the start of the year following the actual value. Extracting Information from Date Numbers Given a date number you may wish to extract numeric values associated with a portion of the value. For example, you might wish to know the value of the month, the year, or the day in the year associated with a given date value. EViews provides the @datepart function to allow you to extract the desired information. • @datepart(date1, u): returns a numeric part of a date value given by u, where u is a time unit string.
- 119. 112—Chapter 5. Strings and Dates Consider the date1 date value 730110.5 (noon, December 23, 1999). The @datepart values for @datepart(730110.5, "dd") @datepart(730110.5, "w") @datepart(730110.5, "ww") @datepart(730110.5, "mm") @datepart(730110.5, "yy") are 23 (day of the month), 1 (day in the week), 52 (week in the year), 12 (month in the year), and 99 (year), respectively. Note that the numeric values returned from @datepart are not themselves date values, but may be used with @makedate to create date values. Special Date Functions In addition to the functions that convert strings or numbers into date values, EViews pro- vides the following special ways to obtain one or more date values of interest. • @now: returns the date number associated with the current time. The remaining functions return information for each observation in the current workfile. • @date: returns the date number corresponding to every observation in the current workfile. • @year: returns the four digit year in which the current observation begins. It is equiv- alent to @datepart(@date, "YYYY") • @quarter: returns the quarter of the year in which the current observation begins. It is equivalent to @datepart(@date, "Q"). • @month: returns the month of the year in which the current observation begins. It is equivalent to @datepart(@date, "MM"). • @day: returns the day of the month in which the current observation begins. It is equivalent to @datepart(@date, "DD"). • @weekday: returns the day of the week in which the current observation begins, where Monday is given the number 1 and Sunday is given the number 7. It is equiva- lent to @datepart(@date, "W"). • @hour: returns the current observation hour as an integer. For example, 9:30AM returns 9, and 5:15PM returns 17. • @minute: returns the current observation minute as an integer. For example, 9:30PM returns 30.
- 120. Dates—113 • @second: returns the current observation second as an integer. • @hourf: returns the current observation time as a floating point hour. For example, 9:30AM returns 9.5, and 5:15PM returns 17.25. • @strdate(fmt): returns the set of workfile row dates as strings, using the date format string fmt. See “Date Formats” on page 97 for a discussion of date format strings. The @date function will generally be used to create a series containing the date value asso- ciated with every observation, or as part of a series expression involving date manipulation. For example: series y = @date series x = @dateadd(@date, 12, "ww") which generates a series containing the date values for every observation, and the date val- ues for every observation 12 weeks from the current values. @strdate should be used when you wish to obtain the date string associated with every observation in the workfile—for example, to be used as input to an alpha series. It is equiv- alent to using the @datestr function on the date number associated with every observation in the workfile. Free-format Conversion Formats EViews supports the free-format conversion of a wide variety of date strings in which the string is analyzed for the most likely corresponding date. Any of the following date string types are allowed: Day, month, year • “"YYYY-MM-DD"” (IEEE, with the date enclosed in double quotes) • “dd/mm/yy” (if American, “mm/dd/yy” instead) • “dd/mm/yyyy” (if American, “mm/dd/yyyy” instead) • “yyyy/mm/dd” • “dd/mon/yy” • “dd/mon/yyyy” • “yyyy/mon/dd” • “ddmmyy” (if American, “mmddyy”) • “ddmmyyyy” (if American, “mmddyyyy”) The resulting date values correspond to the first instance in the day (12 midnight).
- 121. 114—Chapter 5. Strings and Dates Month in year • “mon/yy” • “mon/yyyy” • “yy/mon” • “yyyy/mon” The results are rounded to the first instance in the month (12 midnight of the first day of the month). Period in year • “yyyy[S|Q|M|W|B|D|T|F|:]period” • “yy[S|Q|M|W|B|D|T|F|:]period” The date value is rounded to the first instance in the period in the year Whole year • “yyyy[A]”. The “A” is generally optional, but required if current WF is undated. • “yy[A]”. The “A” is generally optional, but required if current WF is undated. The date value is rounded to the first instance in the year (12 midnight on January 1). Free-format Conversion Details Note that the following conventions may be used in interpreting ambiguous free-form dates. Dates and Date Intervals A date in EViews is generally taken to represent a single point in calendar time. In some con- texts, however, a date specification is used to refer to a range of values contained in a time, which can be referred to as an interval. When a date specification is treated as an interval, the precision with which the date is spec- ified is used to determine the duration of the interval. For example, if a full day specification is provided, such as “Oct 11 1980”, then the interval is taken to run from midnight at the beginning of the day to just before midnight at the end of the day. If only a year is specified, such as “1963”, then the interval is taken to run from midnight on the 1st of January of the year to just before midnight on the 31st of December at the end of the year. An example where this is used is in setting the sample for a workfile. In this context, pairs of dates are provided to specify which observations in the workfile should be included in the sample. The pairs of dates are provided are processed as intervals, and the sample is defined to run from the start of the first interval to the end of the second interval. As an example, if
- 122. Dates—115 the sample “1980q2 1980q2” is specified for a daily file, the sample will include all observa- tions from April 1st 1980 to June 30th 1980 inclusive. Incomplete Date Numbers An EViews date number can be used to represent both a particular calendar day, and a par- ticular time of day within that day. If no time of day is specified, the time of day is set to midnight at the beginning of the day. When no date is specified, the day portion of a date is effectively set to 1st Jan A.D. 1. For example, the date string “12 p.m.” will be translated to the date value 0.5 representing 12 noon on January 1, A.D. 1. While this particular date value is probably not of intrinsic inter- est, it may be combined with other information to obtain meaningful values. See “Manipu- lating Date Numbers” on page 109 Two-digit Years In general, EViews interprets years containing only two digits as belonging to either the twentieth or twenty-first centuries, depending on the value of the year. If the two digit year is greater than or equal to 30, the year is assumed to be from the twentieth century and a century prefix of “19” is added to form a four digit year. If the number is less than 30, the year is assumed to be from the twenty first century and a century prefix of “20” is added to form a four digit year. Note that if you wish to refer to a year after 2029 or a year before 1930, you must use the full four-digit year identifier. Because this conversion to four digit years is generally performed automatically, it is not possible to specify years less than A.D. 100 using two digit notation. Should the need ever arise to represent these dates, such two digit years can be input directly by specifying the year as a four digit year with leading zeros. For example, the 3rd of April in the year A.D. 23 can be input as “April 3rd 0023”. Implicit Period Notation In implicit period notation (e.g., “1990:3”), the current workfile frequency is used to deter- mine the period. American vs. European dates When performing a free-format conversion in the absence of contextual information suffi- cient to identify whether data are provided in “mm/dd” or “dd/mm” format, the global workfile setting for the Options/Dates & Frequency Conversion.../Month/Day order in dates (“Date Representation” on page 824 of the User’s Guide I) will be used to determine the ordering of the days and months in the string. For example, the order of the months and years is ambiguous in the date pair:
- 123. 116—Chapter 5. Strings and Dates 1/3/91 7/5/95 so EViews will use the default date settings to determine the desired ordering. We caution you, however, that using default settings to define the interpretation of date strings is not a good idea since a given date string may be interpreted in different ways at different times if your settings change. You may instead use the IEEE standard format, “YYYY-MM-DD” to ensure consistent interpretation of your daily date strings. The presence of a dash in the for- mat means that you must enclose the date in quotes for EViews to accept this format. For example: smpl "1991-01-03" "1995-07-05" will always set the sample to run from January 3, 1991 and July 5, 1995. Time of Day Free-format dates can also contain optional trailing time of day information which must fol- low the pattern: hh[[[[[:mi:]ss].s]s]s][am|AM|pm|PM] where “[]” encloses optional portions or the format and “|” indicates one of a number of possibilities. In addition, either the “am” or “pm” field or an explicit minute field must be provided for the input to be recognized as a time. An hour by itself is generally not suffi- cient. The time of day in an EViews date is accurate up to a particular millisecond within the day, although any date can always be displayed at a lower precision. When displaying times at a lower precision, the displayed times are always rounded down to the requested precision, and never rounded up. When both a day and a time of day are specified as part of a date, the two can generally be provided one after the other with the two fields separated by one or more spaces. If, how- ever, the date is being used in a context where EViews does not permit spaces between input fields, a single letter “t” can also be used to separate the day and time so that the entire date can be contained in a single word, e.g. “1990-Jan-03T09:53”. Time Zones There are a related set of functions that you may use that provide information on time- zones: @localt (p. 622), @localt (p. 622), @tzlist (p. 635), @tzspec (p. 636), and @utc (p. 637).
- 124. Chapter 6. EViews Programming EViews’ programming features allow you to create and store commands in programs that automate repetitive tasks, or generate a record of your research project. You may, for example, write a program containing commands that analyze the data from one industry, and then have the program perform the analysis for a number of other indus- tries. You can also create a program containing the commands that take you from the cre- ation of a workfile and reading of raw data, through the calculation of your final results, and construction of presentation graphs and tables. The remainder of this chapter outlines the basics of EViews programming. If you have expe- rience with computer programming and batch or macro processing, you will find most of the features of the EViews language to be quite familiar. At the same time, non-programmers should feel welcome to examine the material as you need not have any experience with pro- gramming to take advantage of these powerful features. Program Basics What is a Program? A program is simply a text file containing EViews commands. It is not an EViews object in a workfile. It exists as a file on your computer hard disk, generally with a “.PRG” extension. Creating a Program To create a new program, click File/New/Program. You will see a standard text editing win- dow where you can type in the lines of the program. You may also open the program win- dow by typing program in the command window, followed by an optional program name. For example program firstprg opens a program window named “FIRSTPRG”. Program names should follow standard EViews rules for file names. Program Formatting As noted earlier, an EViews program is a text file, consisting of one or more lines of text. Generally, each line of a program corresponds to a single EViews command, so you may enter the text for each command and terminate the line by pressing the ENTER key. If a program line is longer than the current program window, EViews will, by default, autow- rap the text of the line. Autowrapping alters the appearance of the program line by display- ing it on multiple lines, but does not change the contents of the line. While resizing the
- 125. 118—Chapter 6. EViews Programming window will change the autowrap position, the text remains unchanged and is still con- tained in a single line. You may turn off autowrapping in programs via Options/General Options/Programs and deselecting the Enable word wrap check box, or by clicking the Wrap +/- button on the program window. When autowrapping is turned off via the option menu, you may elect to show program line numbers in your program window by selecting Display line numbers. You may then right- click anywhere in your program and select Go To Line... to jump directly to a specific line number. If you desire greater control over the appearance of your lines, you can manually break long lines using the ENTER key, and then use the underscore continuation character “_” as the last character on the line to join the multiple lines. For example, the three separate lines of text equation eq1.ls _ y x c _ ar(1) ar(2) are equivalent to the single line equation eq1.ls y x c ar(1) ar(2) formed by joining the lines at the continuation character. We emphasize that the “_” must be the very last character in the line. The apostrophe “'” is the comment character in programs. You may place the comment character anywhere in a line to treat all remaining characters in the line as a comment which will be ignored when executing the program command. equation eq1.ls y x c ar(1) ar(2) ’ this is a comment A block of lines may be commented or uncommented in the EViews program file editor by highlighting the lines, right-mouse clicking, and selecting Comment Selection or Uncom- ment Selection. You can instruct EViews to automatically format your program by selecting the lines to which you wish to apply formatting, right-mouse clicking and selecting Format Selection. Automatic formatting will clean up the text in the selection, and will highlight the structure of the program by indenting lines of code inside loops, if conditions and subroutines, etc. You should find that automatic formatting makes your programs easier to read and edit. You may elect to have EViews automatically indent your program as you type by changing the Indent option in the main EViews options menu (Options/General Options/Programs).
- 126. Program Basics—119 Saving a Program After you have created and edited your program, you will probably want to save it. Press the Save or SaveAs button on the program window toolbar. When saved, the program will have the extension “.PRG”. If saving your program will overwrite an existing program file and you have instructed EViews to make backup copies (“Programs,” on page 825 of User’s Guide I), EViews will automatically create the backup. The backup copy will have the same name as the file, but with the first character in the extension changed to “~”. Saving the Command Window One convenient method of creating a program is to execute several commands using the EViews command window and then save the history of those commands to a program file. Click in the command window then select File/Save As... from the main EViews menu. EViews will prompt you to save the command log as a text file. Simply save the file with the “.PRG” extension. You may then edit the program file and save the edited version in the usual fashion. Encrypting a Program EViews offers you the option of encrypting a program file so that you may distribute it to others in a form where they may not view the original text. Encrypted files may be opened and the program lines may be executed, but the source lines may not be viewed. To encrypt a program file simply click on the Encrypt button on the program window. EViews will create an untitled program containing the contents of the original program, but with only the visible text “Encrypted program”. You may save this encrypted program in the usual fashion using Save or SaveAs. Note that once a program is encrypted it may not be unencrypted; encryption should not be viewed as a method of password protecting a program. You should always keep a separate copy of the original source program. To use an encrypted program, simply open it and run the program in the usual fashion. Opening a Program To load a program file previously saved on disk, click on File/Open/Program..., navigate to the appropriate directory, and click on the desired name. You may also drag an EViews pro- gram file onto the EViews window to open it. Alternatively, from the command line, you may type the keyword open followed by the full program name, including the file extension “.PRG”. By default, EViews will look for the program in the default directory, but you may include the full path to the file and enclosed the entire name in quotations, if desired. For example, the commands:
- 127. 120—Chapter 6. EViews Programming open mysp500.prg open "c:mywork is hereeviewsmyhouse.prg" open the file “Mysp500.PRG” in the default EViews directory, and “Myhouse.PRG” located in the directory “c:mywork is hereeviews”. Executing a Program Executing a program is the process of running all of the commands in a program file. Note that EViews commands can be executed in two distinct ways. When you enter and run, line by line, a series of commands in the command window, we say that you are working in interactive mode. Alternatively, you can type all of the commands in a program and execute or run them collectively as a batch of commands. When you run the commands from a pro- gram file, we say that you are in (non-interactive) program mode or batch mode. There are several ways to execute an EViews program: • The easiest method is by pushing the Run button on an open program window and entering settings in the Run Program dialog. • Alternately, you may use the run or exec command to execute a program. • You may use external automation tools to run EViews and execute a program from outside of the EViews application environment. • You may select a set of subset of lines to execute and run the selected lines. The Run Program Dialog To run a program from a dialog, click on the Run button on the program window. The Run dialog opens. The default run options are taken from the global settings (“Programs” on page 825 of User’s Guide I), but may be overridden on a one-time basis using the dialog settings or command options. For purposes of discussion, we will assume that the global options are set to their out-of-the-box values. The Program name or path edit field will show the name and in some cases, path, of the program you are running. You may enter a file specification to instruct EViews to look for a particular program.
- 128. Program Basics—121 The Program arguments edit field is used to define special string variables that will be passed to your program when it is running. See “Program Arguments” on page 136 of User’s Guide I for details. The Runtime errors section allows you to modify the behavior of the program when it encounters an error. By default, when EViews encounters an error, it will immediately termi- nate the program and display a message. If you enter a number into the Maximum errors before halting field, EViews will, if possible, continue to execute the program until the max- imum number of errors is reached. If it encounters a serious error that makes it impossible to continue, EViews will halt the program even if the maximum number of errors is not reached. See “Execution Errors” on page 146. If the Compatibility section checkbox labeled Version 4 compatible variable substitution and program boolean comparisons is selected, EViews will use the variable substitution behavior found in EViews 4 and earlier. To support the use of alpha series, EViews 5 and subsequent versions altered the way that % substitution variables are evaluated in expres- sions. To return to EViews 4 compatible rules for substitution, you may either use this checkbox or include a “MODE VER4” statement in your program. See “Version 4 Compati- bility Notes” on page 169 and “Program Modes” on page 134 for additional discussion. Lastly, you may select the Save options as default checkbox to update your global options with the specified settings. Alternately, you may change the global options from the Options/General Options.../Programs/General dialog. The Run and Exec Commands You may use the run command to execute a program from the EViews command window. Simply enter the keyword run along with any options, followed by a program file specifica- tion and any arguments (see run (p. 467)). You may run a program by entering the run command, followed by the name of the program file: run mysp500 or, using an explicit path, run c:eviewsmyprog arg1 arg2 arg3 Note that use of the “.PRG” extension is not required since EViews will automatically append one to your specification. The default run options will be taken from the global settings (“Programs” on page 825 of User’s Guide I), but may be overridden using the command options. For example, you may use the “v” or “verbose”options to run the program in verbose mode, and the “q” or “quiet” options to run the program in quiet mode. If you include a number as an option, EViews will
- 129. 122—Chapter 6. EViews Programming use that number to indicate the maximum number of errors encountered before execution is halted: run(v, 500) mysp500 or run(q, ver4) progarg Alternatively, you may modify your program to include statements for quiet or verbose mode, and statements to specify version compatibility. For example, to return to EViews 4 compatible rules for substitution, you may use the “ver4” option or include a “MODE VER4” statement in your program. See “Version 4 Compatibility Notes” on page 169 and “Program Modes” on page 134 for additional discussion. You may provide a list of program arguments after the name of the program. These argu- ments will be passed on to the program as %0, %1 etc. See “Program Arguments” on page 136 for more details. Program options may be passed on to the program by entering them, surrounded by paren- thesis immediately after the name of the program. See “Program Options” on page 137 for details. For example: run myprog(opt1, opt2, opt3=k) arg0 arg1 arg1 will run the program MYPROG passing on the options OPT1, OPT2, OPT3=k as options, and ARG0, ARG1 and ARG1 as arguments (%0, %1 and %2 respectively). You may have launch EViews and run a program automatically on startup by choosing File/ Run from the menu bar of the Windows Program Manager or Start/Run in Windows and then typing “eviews”, followed by the name of the program and the values of any argu- ments. If the program has as its last line the command exit, EViews will close following the execution of the program. The exec command is similar to the run command. It can also be used to execute a pro- gram file. The main differences between run and exec commands are the default directory they will run from, and the behavior when returning from executing a program. For more details see “Multiple Program Files” on page 148. External Automation Tools Lastly, you may use external automation tools to run EViews and execute a program from outside of the EViews application environment. In particular, EViews may be used as a COM Automation server so that an external program or script may launch and control EViews pro- grammatically. See “EViews COM Automation Server” on page 177 and the EViews COM Automation Server whitepaper, available from our website www.eviews.com/download/ download.html, for additional discussion.
- 130. Program Basics—123 Stopping a Program Pressing the ESC or F1 keys halts execution of a program. It may take a few seconds for EViews to respond to the halt command. Programs will also stop when they encounter a stop command, when they reach the maxi- mum number of errors, when they encounter a non-recoverable error in a program state- ment, or when they finish processing a file that has been executed via a run statement. If you include the exit keyword in your program, the EViews application will close. Running Part of a Program You may choose to only run part of your program by highlight the lines you wish to run, then right-clicking and selecting Run Selected. EViews will then execute only the selected line of code as a new program. You should note that there are potential pitfalls when using Run Selected. The first is that no arguments or options can be passed into the selected lines of code. If the selected lines rely on program arguments or options, they will fail to execute properly. Similarly, any pro- gram variables (“Program Variables” on page 126) declared outside of the selected lines may not be initialized properly when the selected lines are run. Finally, any subroutines declared outside of the selected lines of code cannot be called, since they do not exist in the selected code. Alternately, you may use add the stop command to your EViews program to halt execution at a particular place in the program file.
- 131. 124—Chapter 6. EViews Programming Simple Programs The simplest program is just a list of EViews commands. Execution of the program is equiv- alent to typing the commands one-by-one into the command window. While you may execute the commands by typing them in the command window, you could just as easily open a program window, type in the commands and click on the Run button. Entering commands in this way has the advantage that you can save the set of commands for later use, and execute the program repeatedly, making minor modifications each time. (Note that you may combine the two methods by entering the commands in the command window and then save them to a file as described in “Saving the Command Window” on page 119.) Since an EViews program is a collection of commands, familiarity with the EViews com- mand language is an essential part of programming. The command language may be split into four different groups in EViews. • General commands create, structure and manipulate workfiles (including importing and exporting data) and create workfile objects. These commands are listed in Chapter 12. “Command Reference,” beginning on page 281. • Object commands execute views and procedures on objects in the workfile (such as series, groups and equations). For example, unit root tests are done via a series command (series.uroot), co-inte- gration tests are a group command (group.coint), and regression and forecasting are performed via equation commands (equation.ls and equation.forecast). The full set of object commands can be found in Chapter 1. “Object View and Proce- dure Reference,” beginning on page 2, where they are listed by object type. • Functions may be used to create new data, either in the form of workfile objects or as program variables. Creating a new series that is equal to the moving average of an existing series is done with the @movav function. The @rbinom function is used to generate a random scalar or series from the binomial distribution. @pagefreq may be used to generate a string object or program variable containing the frequency of the current workfile page. In general, functions all start with the @ symbol. Listings of the available @-functions available can be found in Chapters 13–19 (“Operator and Function Reference”, “Operator and Function Listing”, “Workfile Functions”, “Special Expression Reference”, “String and Date Function Reference”, “Matrix Language Reference”, and “Programming Language Reference”). • Object data members allow you to retrieve a fundamental piece of data from an object. Unlike object commands that execute a view or procedure on an object, data members merely provide access to existing results. Data members almost all start with
- 132. Simple Programs—125 an @ symbol and can be accessed by typing the name of the object followed by a “.” and the name of the data member. For example, eq1.@coefs returns a vector containing the estimated coefficients from the equation object EQ1. ser01.@last returns a string containing the date (or obser- vation number) of the last non-NA value in the series SER01. The list of data members available for each object is listed in the object’s section of Chapter 1. “Object View and Procedure Reference,” on page 2. Let’s examine a simple example (the data series are provided in the database PROGDEMO in your EViews directory so that you can try out the program). Create a new program by typing program myprog in the command window. In the program window that opens for MYPROG, we are going to enter the commands to create a workfile, fetch a series from an EViews database named PROGDEMO, run a regression, compute residuals and a forecast, make a plot of the forecast, and save the results. ' housing analysis wfcreate(wf=myhouse) m 1968m3 1997m6 fetch progdemo::hsf smpl 1968m5 1992m12 equation reg1.ls hsf c hsf(-1) reg1.makeresid hsfres smpl 1993m1 1997m6 reg1.forecast hsffit freeze(hsfplot) hsffit.line save The first line of the program is a comment, as denoted by the apostrophe “'”. In executing a program, EViews will ignore all text following the apostrophe until the end of the line. The line containing the wfcreate command creates a new workfile, called MYHOUSE, with a monthly frequency spanning the dates March 1968 to June 1997. A series called HSF (total housing units started) is fetched from the database PROGDEMO. The rest of the program results in a saved workfile named MYHOUSE containing the HSF series, an equation object REG1, residual and forecast series HSFRES and HSFFIT, and a graph HSFPLOT of the fore- casts. You can run this program by clicking on Run and filling in the dialog box. Now, suppose you wish to perform the same analysis, but for the S&P 500 stock price index (FSPCOM). Edit the program, changing MYHOUSE to MYSP500, and change all of the refer- ences of HSF to FSPCOM: ' s&p analysis
- 133. 126—Chapter 6. EViews Programming wfcreate(wf=mysp500) m 1968m3 1997m6 fetch progdemo::fspcom smpl 1968m5 1992m12 equation reg1.ls fspcom c fspcom(-1) reg1.makeresid fspcomres smpl 1993m1 1997m6 reg1.forecast fspcomfit freeze(fscomplot) fspcomfit.line save Click on Run to execute the new analysis. Click on the Save button to save your program file as MYPROG.PRG in the EViews directory. Since these two programs are almost identical, it seems inefficient to have two separate pro- grams for performing the analysis. In “Program Arguments” on page 136 we describe a method for performing these two forecasting problems using a single program. First how- ever, we must define the notion of program variables that exist solely when running pro- grams. Program Variables While you can use programs just to run, and re-run collections of EViews commands, the real power of the EViews programming language comes from the use of program variables and program control statements. Program variables are variables that you may use in place of numeric or string values in your EViews programs. Accordingly, there are two basic types of program variables: control (numeric) variables and string variables. In the remainder of this section we describe the use of these two types of program variables. Control Variables Control variables are program variables that you can use in place of numeric values in your EViews programs. Once a control variable is assigned a value, you can use it anywhere in a program that you would normally use a number. It is important to note that control variables do not exist outside of your program and are automatically deleted after a program finishes. In this regard, control variables should not be confused with scalar objects as the former are not saved when you save the workfile. You can save the values of control variables by creating new EViews objects which contain the values of the control variable.
- 134. Program Variables—127 The name of a control variable starts with an “!” character. After the “!”, the name should be a legal EViews name of 23 characters or fewer (leading numbers are allowed). Examples of control variable names are: !x !1 !counter You need not declare control variables, but you must assign them values before use. Control variable values are assigned in the usual way, with the control variable name on the left of an “=” sign and a numerical value or expression on the right. For example: !x = 7 !12345 = 0 !counter = 12 !pi = 3.14159 Once assigned a value, a control variable may appear in an expression. For example: !counter = !counter + 1 genr dnorm = 1/sqr(2*!pi)*exp(-1/2*epsilon^2) scalar stdx = x/sqr(!varx) smpl 1950q1+!i 1960q4+!i For example, the following commands: scalar stdx = sqr(!varx) c(100) = !length sample years 1960+!z 1990 use the numeric values assigned to the control variables !VARX, !LENGTH, and !Z. It is important to note that control variables are used in programs via text substitution. Whenever EViews encounters a control variable in a program it substitutes the text value of that variable into the program. One of the implications of the text substitution is that you may lose some numeric accuracy when using a program variable due to the conversion in to and out of a text representation of the number. A second unintended consequence of text substitution can arise when raising a negative control variable to a power: !a = -3 !b = !a^2 When evaluating these lines, EViews will substitute the value of !A in the second line, leav- ing the line: !b = -3^2
- 135. 128—Chapter 6. EViews Programming Which it then evaluates as –9 (since the square operator takes precedence over the negation operator), rather than the expected 9. You should also take care to avoid inadvertent replacement of program variables, as outlined in “Replacement Variables” on page 130. String Variables A string expression or string is text enclosed in double quotes: "gross domestic product" "3.14159" "ar(1) ar(2) ma(1) ma(2)" A string program variable is a program variable whose value is a string of text. String vari- ables, which exist only during the time that your program is executing, have names that begin with a “%” symbol. String variables should not be confused with “String Objects” on page 92 which are objects that exist in a workfile. String variables are assigned by putting the string variable name on the left of an “=” sign and a string expression on the right. For example, the following lines assign values to string variables: %value = "value in millions of u.s. dollars" %armas = "ar(1) ar(2) ma(1) ma(2)" %mysample = " 83m1 96m12" %dep = " hs" %pi = " 3.14159" You may use strings variables to help you build up command text, variable names, or other string values. EViews provides a number of operators and functions for manipulating strings; a complete list is provided in “Strings” on page 77. Once assigned a value, a string variable may appear in any expression in place of the under- lying string. When substituted for, the string variable will be replaced by the contents of the string variable, enclosed in double quotes. Here is a simple example where we use string operations to concatenate the contents of three string variables. !repeat = 500 %st1 = " draws from the normal" %st2 = "Cauchy " %st3 = @str(!repeat) + @left(%st1,16) + %st2 + "distribution"
- 136. Program Variables—129 In this example %ST3 is set to the value “500 draws from the Cauchy distribution”. Note the spaces before “draws” and after “Cauchy” in the string variable assignments. After string variable substitution, the latter assignment is equivalent to entering %st3 = "500" + " draws from the " + "Cauchy " + "distribution" Similarly, the table assignment statement table1(1,1) = %st3 is equivalent to entering the command table(1,1) = "500 draws from the Cauchy distribution" One important use for string variables is assigning string values to string objects, string vec- tors, or Alpha series. For example, we may have the assignment statement %z = "Ralph" alpha full_name = %z + last_name which is equivalent to the expression alpha full_name = "Ralph" + last_name We again emphasize that string variable substitution involves replacing the string variable by its string value contents, enclosed in double quotes. As with any string value, you may convert a string variable containing the text representa- tion of a number into a number by using the @val function. For example, %str = ".05" !level = @val(%str) creates a control variable !LEVEL=0.05. If the string cannot be evaluated to a number, @val returns the value “NA”. String variables are closely related to the concept of a string object (“String Objects,” on page 92). A string object is an EViews workfile object that holds a string: string a = "Hello World" string b = """First Name"" Middle ""Last Name""" Unlike string variables, string objects are named objects in the workfile that may exist apart from a running program. In programming applications, string variables and string objects are sometimes defined using string literal expressions as illustrated above. However, in most settings, string vari- ables and objects are defined using results from string functions, or by manipulation of string lists and string vectors. For example, you could use the @wlookup and @wsplit func- tions to obtain a set of strings corresponding to the names of all of the series in a workfile. See “Strings,” on page 77 for a full discussion.
- 137. 130—Chapter 6. EViews Programming Replacement Variables When working with EViews commands, you may wish to use a string variable, not simply to refer to a string value, but as an indirect way of referring to something else, perhaps a com- mand, or an object name, or portion of names for one or more items. Suppose, for example, that we assign the string variable %X the value “GDP”: %x = "gdp" We may be interested, however, not in the actual string variable value “gdp”, but rather in an EViews object named “GDP”. Accordingly, we require a method of distinguishing between a string value and the item corresponding to the string value. If you enclose a string variable in curly braces (“{ ” and “}”) EViews will replace the expres- sion with the name, names, or name fragment given by the string value. In this context we refer to the expression “{%X}” as a replacement variable since the string variable %X is replaced in the command line by the name or names of objects to which the string refers. Suppose we want to create the series Y and set it equal to the values in the series GDP. Given the string variable %X defined above, the series declaration, series y = %x creates a numeric series Y and sets it equal to the string value “gdp”, series y = "gdp" which generates an error since the series declaration expects the name of a series or a numeric value, not a string value. In this circumstance, we would like to replace the string value with the name of an object. With the replacement variable “{%X}”, the command series y = {%x} is properly interpreted as series y = gdp Similarly, the program line using replacement variables, equation eq1.ls {%x} c {%x}(-1) would be interpreted by EViews as equation eq1.ls gdp c gdp(-1) Changing the contents of %X to “M1” changes the interpretation of the original program line to equation eq1.ls m1 c m1(-1) since the replacement variable uses the name obtained from the new value of %X.
- 138. Program Variables—131 To take another example, when trying to find the number of valid (non-missing) observa- tions in a series named INCOME, you may use the @obs function along with the name of the series: @obs(income) If you wish to use a string variable %VAR to refer to the INCOME series, you must use the replacement variable in the @obs function, as in %var = "income" @obs({%var}) since you wish to refer indirectly to the object named in %VAR. Note that the expression @obs(%var) will return an error since @obs requires a series or matrix object name as an argument. Any string variable may be used as the basis of a replacement variable. Simply form your string using one or more string operations %object = "group" %space = " " %reg1 = "gender" %reg2 = "income" %reg3 = "age" %regs = %reg1 + %space + %reg2 + %space + %reg3 then enclose the string variable in braces. In the expression, {%object} g1 {%regs} EViews will substitute the names found in %OBJECT and %REGS so that the resulting com- mand is group g1 gender income age It is worth noting that replacement variables may be used as building blocks to form object names. For example, the commands %b = "2" %c = "temp" series z{%b} matrix(2, 2) x{%b} vector(3) x_{%c}_y declare a series named Z2, a matrix named X2, and a vector named X_TEMP_Y. Up to this point, we have focused on replacement variables formed from string variables. However, control variables may also be used to form replacement variables. For example, the commands 2 2
- 139. 132—Chapter 6. EViews Programming !i = 1 series y{!i} = nrnd !j = 0 series y{!j}{!i} = nrnd are equivalent to series y1 = nrnd series y01 = nrnd and will create two series Y1 and Y01 that contain a set of (pseudo-)random draws from a standard normal distribution. Conveniently, in cases where there is no possibility of ambigu- ity, EViews will treat a control variable as a replacement variable, even if the braces are not provided. For example: !x = 3 series y!x = 4 will generate the series Y3 containing the value 4. While convenient, this loose interpretation of control variables can, however, lead to unex- pected results if one is not careful. Take the following program: !x1 = 10 series y = !x1 This program will create a series equal to the value of !X1 (i.e. 10). However if you were to mis-type the program slightly: !x = 10 series y = !x1 where the first line has !X rather than !X1, EViews will not generate an error due to the missing !X1 variable, but will instead evaluate the second line by substituting !X into the expression, and evaluating the result as 101. Replacement variables may be constructed using nested evaluation of replacement variables. Suppose we have the strings: %str1 = "x" %name = "%str1" Then we can declare the series X and fill it with random normals using the command series {{%name}} = nrnd After evaluation of the innermost replacement variable, the expression reduces to series {%str1} = nrnd and then to
- 140. Program Variables—133 series x = nrnd when we evaluate the remaining brace. The double braces allow us perform a double replacement in which the string variable used to form a replacement variable is itself obtained as a replacement variable. The double nested braces need not be adjacent. For example: %x1 = "x" %x2 = "y" scalar x = 10 scalar y = 20 !y = 1 scalar r1 = {%x{!y}} !y = 2 scalar r2 = {%x{!y}} First we create two string variables, %X1 and %X2, and three scalar objects, X, Y, and R. First, the control variable is !Y is set to 1 and the replacement variable {!Y} is used to con- struct the name “%X1” of a string variable. The resulting replacement variable {%X1} refers to the scalar object X. We assign the scalar X value of 10 to the scalar R1. Next, we set !Y to 2, the replacement variable {%X{!Y}} evaluates to Y, and we assign the Y value of 20 to the scalar R2. Multiple sets of braces may be used to create various string names: string uslabel = "USA" string nzlabel = "New Zealand" %a1 = "US" %a2 = "NZ" %b = "LABEL" !y = 1 string label1 = {%a{!y}}{%b} !y = 2 string label2 = {%a{!y}}{%b} First we create two string objects, USLABEL and NZLABEL, which hold the label we wish to assign to each country. Then we create the string variables %A1 and %A2 to hold the coun- try abbreviations. When the control variable !Y=1, {%A{!Y}}{%B} evaluates to the object USLABEL and when !Y=2, {%A{!Y}}{%B} evaluates to the object NZLABEL. Then the string LABEL1 contains “USA” and LABEL2 contains “New Zealand”. Replacement variables may also be formed using string objects in place of string variables. To use a string object as a replacement variable, simply surround it with curly braces (“{” and “}”). For example,
- 141. 134—Chapter 6. EViews Programming string LAGGDP = "GDP(-1) GDP(-2) GDP(-4)" equation eq1.ls GDP C {LAGGDP} executes the command equation eq1.ls GDP C GDP(-1) GDP(-2) GDP(-4) In most respects, there is no difference between using a string object or a string variable in a program. String objects have the advantage that they may be used interactively, outside of programs, while string variables may not. This can be useful when debugging a program; string variables disappear once the program has finished, making it difficult to identify prob- lems in their construction. Note that string objects do, however, go out-of-scope when the active workfile page changes, while string variables are always in scope. Lastly, while replacement variables provide you with great flexibility in referencing objects in your programs, used carelessly, they can lead to confusion. We suggest, for example, that you avoid using similar base names to refer to different objects. Consider the following pro- gram: ' possibly confusing commands (avoid) !a = 1 series x{!a} !a = 2 matrix x{!a} In this code snippet, it is easy to see that X1 is the series and X2 is the matrix. But in a more complicated program, where the control variable assignment !A=1 may be separated from the declaration by many program lines, it may be difficult to tell at a glance what kind of object X1 represents. A better approach might be to use different names for different kinds of variables: !a = 1 series ser{!a} !a = 2 matrix mat{!a} so that the meaning of replacement variable names are more apparent from casual examina- tion of the program. Program Modes Program modes describe different settings associated with the execution of your program. You may, for example, choose to provide verbose messaging where your program will echo each command to the status line, or you may choose quiet mode. Alternately, you may wish to run a legacy program file in Version 4 compatibility mode.
- 142. Program Modes—135 EViews provides you with the opportunity to set program execution modes at the time that the program is first run. In addition, you may use the “MODE” statement to change the exe- cution mode of a program from within the program itself. One important benefit to using “MODE” statements is that the program can begin executing in one mode, and switch to a second mode as the program executes. To change the mode for quiet or verbose mode, simply add a line to your program reading “MODE” followed by either the “QUIET” or the “VERBOSE” keyword, as in mode quiet For version 4 compatibility, you should use the keyword “VER4”: mode ver4 as a line in the body of the program. Multiple settings may be set in a single “MODE” line: mode quiet ver4 and multiple mode statements may be specified in a program to change the mode as the pro- gram runs: mode quiet [some program lines] mode verbose [additional program lines] Note that setting the execution mode explicitly in a program overrides any settings specified at the time the program is executed. Program Message Logging When executing a program in EViews, it may be useful to keep track of what is happening during execution. Log windows allow you to determine more accurately the state of various objects in your workfile or follow program progression. Log windows may be switched on using the logmode (p. 407) command. One log window is created for each program. If a program is executed more than once and a log window has already been created, the log window will be cleared and all subsequent messages will be sent to the existing log window. If you wish to preserve a log, you may either save the log to a text file or freeze it, creating a text file object. There are several types of messages which can be logged: program lines, status line mes- sages, user log messages, and program errors. When displayed in a log message, each type will appear in a different color, making it easier to differentiate one type from another. Pro- gram lines are echoes of the line of code in the program currently being executed, and are displayed in black. Status line messages are the messages displayed in the status line (see
- 143. 136—Chapter 6. EViews Programming “The Status Line” on page 9 of User’s Guide I) and appear in blue. User log messages are custom messages created by the program via the logmsg (p. 409) command and appear in green. Program errors are messages generated when a logical or syntax error has occurred and appear in red. Program Arguments Program arguments are special string variables that are passed to your program when you run the program. Arguments allow you to change the value of string variables every time you run the program. You may use them in any context where a string variable is appropri- ate. Any number of arguments may be included in a program. The individual arguments will be available in the string variables “%0”, “%1”, “%2”, and so on; the “%ARGS” variable will contain a space delimited list of all the arguments passed into the program. When you run a program that takes arguments, you may supply the values for the argu- ments. If you use the Run button or File/Run, you will see a dialog box where you can type in the values of the arguments. If you use the run or exec command, you may list the argu- ments consecutively after the name of the program. For example, suppose we have a program named “REGPROG”: equation eq1 smpl 1980q3 1994q1 eq1.ls {%0} c {%1} {%1}(-1) time To run REGPROG from the command line with %0=“lgdp” and %1=“m1”, we enter run regprog lgdp m1 This program performs a regression of the variable LGDP, on C, M1, M1(-1), and TIME, by executing the command: eq1.ls lgdp c m1 m1(-1) time Alternatively, you can run this program by clicking on the Run button on the program win- dow, or selecting File/Run.... In the Run Program dialog box that appears, type the name of the program in the Program name or path field and enter the values of the arguments in the Program arguments field. For this example, type “regprog” for the name of the program, and “lgdp” and “m1” for the arguments. Any arguments in your program that are not initialized in the Run Program dialog or run command are treated as empty string variables. For example, suppose you have a one-line program named “REGRESS”: equation eq1.ls y c time {%0} {%1} {%2} {%3} {%4} {%5} {%6} {%7} {%8} The command,
- 144. Program Options—137 run regress x x(-1) x(-2) executes equation eq1.ls y c time x x(-1) x(-2) while the command, run regress executes ls y c time In both cases, EViews ignores arguments that are not included in your run command. As a last example, we repeat our simple forecasting program from above, but use arguments to simplify our work. Suppose you have the program “MYPROG”: wfcreate(wf={%0}) m 1968m3 1997m6 fetch progdemo::{%1} smpl 1968m5 1992m12 equation reg1.ls {%1} c {%1}(-1) reg1.makeresid {%1}res smpl 1993m1 1997m6 reg1.forecast {%1}fit freeze({%1}plot) {%1}fit.line save The results of running the two example programs at the start of this chapter can be dupli- cated by running MYPROG with arguments: run myprog myhouse hsf and run myprog mysp500 fspcom Program Options Much like program arguments, program options are special string variables that may be passed to your program when you run the program. You may specify options by providing a comma separated list of options in parentheses in the run or exec command statement, immediately after the name of the program as in: run myprogram(myoption1, myoption2) Note that options are only supported via the command line method using run or exec. You cannot pass an option to a program when running a program via the menus. Options are included via the command line by entering them in parenthesis immediately following the name of the program to be run.
- 145. 138—Chapter 6. EViews Programming In contrast with arguments, options may not be accessed directly from within your program. Rather you can only test for the existence of an option, or retrieve part of an option. The @hasoption command lets you test for the existence of an option. For example, @hasop- tion("k") will return a value of 1 if the “k” option was passed into the program at run time, or 0 if it was not. @equaloption may be used to return the value to the right of an equality sign in an option. For example if “cov=hac” is entered as an option, @equaloption("cov") would return “hac”. If the option was not entered at all, @equaloption will return an empty string. For example, suppose you have the following program: %filename = @equaloption("file") wfcreate(wf={%filename}) m 1968m3 1997m6 fetch progdemo::{%0} if (@hasoption("LS")=1) then smpl 1968m5 1992m12 equation reg1.ls {%0} c {%0}(-1) endif If you were to run this program with: run myprog(file=myhouse, ls) hsf the program would create a workfile called MYHOUSE, would fetch a series called HSF, and would then create an equation called REG1 by performing least squares using the series HSF (for discussion of the “if” condition used in this example, see “IF Statements” on page 139). If we had run the program with just: run myprog hsf the workfile would not have been named (and would be given the default name of UNTI- TLED), and the regression would not have been run. Note that if your program name has spaces or illegal characters, it must be enclosed within quotes in run or exec commands. In this case, program options should be included after the closing quote without a space. For example, if we were to name our above program as MY PROG, then the correct method to issue options is: run "my prog"(file=myhouse, ls) hsf Control of Execution EViews provides you with several ways to control the execution of commands in your pro- grams. Controlling execution in your program means that you can execute commands selec- tively or repeatedly under changing conditions. The tools for controlling execution will be familiar from other computer languages.
- 146. Control of Execution—139 IF Statements There are situations where you may want to execute commands only if some condition is satisfied. EViews uses IF and ENDIF, or IF, ELSE, and ENDIF statements to indicate the con- dition to be met and the commands to be executed. An IF statement starts with the if keyword, followed by an expression for the condition, and then the word then. You may use AND/OR statements in the condition, using parenthe- ses to group parts of the statement as necessary. All comparisons in the IF statement follow the rules outlined in “String Relational Opera- tors” on page 79 and in “Numeric Relational Operators” on page 172 of User’s Guide I. Note in particular that string comparisons are case-sensitive. You may perform caseless compari- son using the @upper or @lower string functions as in if (@lower(%x) = "abc") then or if (@upper(%x) = "ABC") then If the expression is true, all of the commands until the matching endif are executed. If the expression is false, all of these commands are skipped. For example, if !stand=1 or (!rescale=1 and !redo=1) then series gnpstd = gnp/sqr(gvar) series constd = cons/sqr(cvar) endif if !a>5 and !a<10 then smpl 1950q1 1970q1+!a endif only generates the series GNPSTD and CONSTD and sets the sample if the corresponding IF statements are true. Note that we have indented the lines between the if and the endif state- ments. The indentation is added for program clarity and has no effect on the execution of the program lines. The expression to be tested may also take a numerical value. In this case, 0 and NA are equivalent to false and any other non-zero value evaluates to true. For example, if !scale then series newage = age/!scale endif executes the series statement if !SCALE is a non-missing, non-zero value. An IF statement may include a matching ELSE clause containing commands to be executed if the condition is FALSE. If the condition is true, all of the commands up to the keyword
- 147. 140—Chapter 6. EViews Programming else will be executed. If the condition is FALSE, all of the commands between else and endif will be executed. For example: if !scale>0 then series newage = age/!scale else series newage = age endif (It is worth noting that this example performs a conditional recode in which the series NEW- AGE is assigned using one expression if a condition is true, and a different expression other- wise. EViews provides a built-in @recode function for performing this type of evaluation; see @recode(s,x,y) (p. 550).) IF statements may also be applied to string variables: if %0="CA" or %0="IN" then series stateid = 1 else if %0="MA" then series stateid=2 else if %0="IN" then series stateid=3 endif endif endif Note that the nesting of our comparisons does not cause any difficulties. You should note when using the IF statement with series or matrix objects that the compari- son is defined on the entire object and will evaluate to false unless every element of the ele- ment-wise comparison is true. Thus, if X and Y are series, the IF statement if x<>y then [some program lines] endif evaluates to false if any element of X does not equal the corresponding value of Y in the default sample. If X and Y are identically sized vectors or matrices, the comparison is over each of the elements X and Y. This element-wise comparison is described in greater detail in “Relational Operators (=, >, >=, <, <=, <>)” on page 271. If you wish to operate on individual elements of a series on the basis of element-wise condi- tions, you should use the @recode function or use smpl statements to set the observations
- 148. Control of Execution—141 you wish to change. Element-wise operations on a vector or matrix should use comparisons of individual element comparisons if x(3)=7 then x(3) = 2 endif or the element-wise matrix functions (“Matrix Element Functions” on page 655). The FOR Loop The FOR loop allows you to repeat a set of commands for different values of numerical or string variables. The FOR loop begins with a for statement and ends with a next state- ment. Any number of commands may appear between these two statements. The syntax of the FOR statement differs depending upon whether it uses numerical variables or string variables. FOR Loops with Numerical Control Variables or Scalars To repeat statements for different values of a control variable, you should follow the for key- word by a control variable initialization, the word to, and then an ending value. After the ending value you may include the word step followed by a number indicating an amount to change the control variable each time the loop is executed. If you don’t include step, the step is assumed to be 1. Consider, for example the loop: for !j=1 to 10 series decile{!j} = (income<level{!j}) next In this example, STEP=1 and the variable !J is twice used as a replacement variable, first for the ten series declarations DECILE1 through DECILE10 and for the ten variables LEVEL1 through LEVEL10. We may add the step keyword and value to the FOR loop to modify the step: for !j=10 to 1 step -1 series rescale{!j}=original/!j next In this example, the step is -1, and !J is used as a replacement variable in naming the ten constructed series RESCALE10 through RESCALE1 and as a control variable scalar divisor in the series ORIGINAL. The commands inside the FOR loop are first executed for the initial control value (unless that value is already beyond the terminal value). After the initial execution, the control vari- able is incremented by step and EViews compares the new control variable value to the limit. If the limit is exceeded, execution stops.
- 149. 142—Chapter 6. EViews Programming One important use of FOR loops with control variables is to change the workfile sample using a smpl command. If you add a control variable to a date in a smpl command state- ment, you will get a new date as many observations forward as the current value of the con- trol variable. Here is a FOR loop that gradually increases the size of the sample and estimates a recursive regression: for !horizon=10 to 72 smpl 1970m1 1970m1+!horizon equation eq{!horizon}.ls sales c orders next One other important case uses loops and control variables to access elements of a matrix object. For example, !rows = @rows(vec1) vector cumsum1 = vec1 for !i=2 to !rows cumsum1(!i) = cumsum1(!i-1) + vec1(!i) next computes the cumulative sum of the elements in the vector VEC1 and saves it in the vector CUMSUM1. To access an individual element of a series, you will need to use the @elem function and @otod to get the desired element for !i=2 to !rows cumsum1(!i) = @elem(ser1, @otod(!i)) next The @otod function returns the date associated with the observation index (counting from the beginning of the workfile), and the @elem function extracts the series element associ- ated with a given date. You may nest FOR loops to contain loops within loops. The entire inner FOR loop is exe- cuted for each successive value of the outer FOR loop. For example: matrix(25,10) xx for !i=1 to 25 for !j=1 to 10 xx(!i,!j)=(!i-1)*10+!j next next You should avoid changing the control variable within a FOR loop. Consider, for example, the commands:
- 150. Control of Execution—143 ' potentially confusing loop (avoid doing this) for !i=1 to 25 vector a!i !i=!i+10 next Here, both the FOR assignment and the assignment statement within the loop change the value of the control variable !I. Loops of this type are difficult to follow and may produce unintended results. If you find a specific need to change a control variable inside the loop, you should consider using a WHILE loop (“The WHILE Loop” on page 145) as an alterna- tive to the FOR loop. You may execute FOR loops with scalars instead of control variables. However, you must first declare the scalar, and you may not use the scalar as a replacement variable. For exam- ple, scalar i scalar sum = 0 vector (10) x for i=1 to 10 x(i) = i sum = sum + i next In this example, the scalar objects I and SUM remain in the workfile after the program has finished running, unless they are explicitly deleted. When using scalar objects as the looping variable you should be careful that the scalar is always available white the FOR loop is active. You should not, for example, delete the scalar or change the workfile page within the FOR loop. FOR Loops with String Variables and String Objects To repeat statements for different values of a string variable, you may use the FOR loop to let a string variable range over a list of string values. You should list the FOR keyword, followed by the name of the string program variable, followed by the list of values. For example, for %y gdp gnp ndp nnp equation {%y}trend.ls {%y} c {%y}(-1) time next executes the commands equation gdptrend.ls gdp c gdp(-1) time equation gnptrend.ls gnp c gnp(-1) time equation ndptrend.ls ndp c ndp(-1) time equation nnptrend.ls nnp c nnp(-1) time
- 151. 144—Chapter 6. EViews Programming You may include multiple string variables in the same FOR statement—EViews will process the string values in sets. For example, we may define a loop with list three string variables: for %1 %2 %3 1955q1 1960q4 early 1970q2 1980q3 mid 1975q4 1995q1 late smpl %1 %2 equation {%3}eq.ls sales c orders next In this case, the elements of the list are taken in groups of three. The loop is executed three times for the different sample pairs and equation names: smpl 1955q1 1960q4 equation earlyeq.ls sales c orders smpl 1970q2 1980q3 equation mideq.ls sales c orders smpl 1975q4 1995q1 equation lateeq.ls sales c orders Both string objects and replacement variables may be used to specify a list for use in loops, by surrounding the object name or replacement variable with curly braces (“{ }”). For example, string dates = "1960m1 1960m12" %label = "year1" for %1 %2 %3 {dates} {%label} smpl {%1} {%2} equation {%3}eq.ls sales c orders next finds the three strings for the loop by taking two elements of the string list and one element of the string variable: smpl 1960m1 1960m12 equation year1eq.ls sales c orders Note the difference between using a FOR loop with multiple string variables and using nested FOR loops. In the multiple string variable case, all string variables are advanced at the same time, while with nested loops, the inner variable is advanced over all choices, for each value of the outer variable. For example: !eqno = 1 for %1 1955q1 1960q4 for %2 1970q2 1980q3 1975q4 smpl %1 %2 'form equation name as eq1 through eq6
- 152. Control of Execution—145 equation eq{!eqno}.ls sales c orders !eqno=!eqno+1 next next Here, the equations are estimated over the samples 1955Q1–1970Q2 for EQ1, 1955Q1– 1980Q3 for EQ2, 1955Q1–1975Q4 for EQ3, 1960Q4–1970Q2 for EQ4, 1960Q4–1980Q3 for EQ5, and 1960Q4–1975Q4 for EQ6. Note that you may use the exitloop command to exit a FOR loop early. See “Exiting Loops” on page 147. The WHILE Loop In some cases, we wish to repeat a series of commands several times, but only while one or more conditions are satisfied. Like the FOR loop, the WHILE loop allows you to repeat com- mands, but the WHILE loop provides greater flexibility in specifying the required conditions. The WHILE loop begins with a while statement and ends with a wend statement. Any num- ber of commands may appear between the two statements. WHILE loops can be nested. The WHILE statement consists of the while keyword followed by an expression involving a control variable or scalar object. The expression should have a logical (true/false) value or a numerical value. In the latter case, zero is considered false and any non-zero value is consid- ered true. If the expression is true, the subsequent statements, up to the matching wend, will be exe- cuted, and then the procedure is repeated. If the condition is false, EViews will skip the fol- lowing commands and continue on with the rest of the program following the wend statement. For example: !val = 1 !a = 1 while !val<10000 and !a<10 smpl 1950q1 1970q1+!a series inc{!val} = income/!val !val = !val*10 !a = !a+1 wend There are four parts to this WHILE loop. The first part is the initialization of the control vari- ables used in the test condition. The second part is the WHILE statement which includes the test. The third part is the statements updating the control variables. Finally the end of the loop is marked by the word wend.
- 153. 146—Chapter 6. EViews Programming Unlike a FOR statement, the WHILE statement does not update the control variable used in the test condition. You need to include an explicit statement inside the loop to change the control variable, or your loop will never terminate. Use the F1 key to break out of a program which is in an infinite loop. Earlier, we cautioned against this behavior creating FOR loops that explicitly change the control variable inside the loop and offered an example to show the resulting lack of clarity (p. 142). Note that the equivalent WHILE loop provides a much clearer program: !i = 1 while !i<=25 vector a{!i} !i = !i + 11 wend Note that you may use the exitloop command to exit a WHILE loop early. See “Exiting Loops” on page 147. Execution Errors By default, EViews will stop executing after encountering any errors. You can instruct the program to continue running even if errors are encountered by changing the maximum error count from the Run dialog (see “Executing a Program” on page 120), or by using the set- maxerrs (p. 721) command inside a program. Handling Errors You may wish to perform different tasks when errors are encountered. For example, you may wish to skip a set of lines which accumulate estimation results when the estimation proce- dure generated errors, or you may wish to overwrite the default EViews error with one of your own, using the seterr (p. 720) command. EViews offers a number of different ways to test for and handle execution errors. For exam- ple, the @lasterrstr (p. 715) command will return a string containing the previous line's error. If the previous line of your program did not error, this string will be empty. Alterna- tively you could use the @errorcount (p. 709) function to check the number of errors cur- rently encountered before and after a program line has been executed. For example, to test whether the estimation of an equation generated an error, you can com- pare the number of errors before and after the command: !old_count = @errorcount equation eq1.ls y x c !new_count = @errorcount if !new_count > !old_count then [various commands]
- 154. Control of Execution—147 endif Here, we perform a set of commands only if the estimation of equation EQ1 incremented the error count. For additional error handling functions, see “Support Commands” on page 701 and “Support Functions” on page 702. Stopping Programs Occasionally, you may want to stop a program based on some conditions. To stop a program executing in EViews, use the stop command. For example, suppose you write a program that requires the series SER1 to have nonnegative values. The following commands check whether the series is nonnegative and halt the program if SER1 contains any negative value: series test = (ser1<0) if @sum(test) <> 0 then stop endif Note that if SER1 contains missing values, the corresponding elements of TEST will also be missing. But since the @sum function ignores missing values, the program does not halt for SER1 that has missing values, as long as there is no negative value. Exiting Loops Sometimes, you do not wish to stop the entire program when a condition is satisfied; you just wish to exit the current loop. The exitloop command will exit the current for or while statement and continue running the program. As a simple example, suppose you computed a sequence of LR test statistics LR11, LR10, LR9, ..., LR1, say to test the lag length of a VAR. The following program sequentially carries out the LR test starting from LR11 and tells you the statistic that is first rejected at the 5% level: !df = 9 for !lag = 11 to 1 step -1 !pval = 1 - @cchisq(lr{!lag},!df) if !pval<=.05 then exitloop endif next scalar lag=!lag Note that the scalar LAG has the value 0 if none of the test statistics are rejected.
- 155. 148—Chapter 6. EViews Programming If the exitloop is issued inside nested loops it will stop execution of the innermost loop. Execution of the remaining loops is unaffected. Multiple Program Files When working with long programs, you may wish to organize your code using multiple files. For example, suppose you have a program file named “Powers.PRG” which contains a set of program lines that you wish to use. While you may be tempted to string files together using the run command, we caution you that EViews will stop after executing the commands in a run-referenced file. Thus, a pro- gram containing the lines run powers.prg series x = nrnd will only execute the commands in the file “Powers.PRG”, and will stop before generating the series X. This behavior is probably not what you intended. The exec command may be used execute commands in a file in place of the run command. Though exec is quite similar to the run command, there are important differences between the two commands: • First, exec allows you to write general programs that execute other programs, some- thing that is difficult to do using run, since the run command ends all program exe- cution when processing of the named file is completed. In contrast, once exec processing completes, the calling program will continue to run. • Second, the default directory for exec is the Add-ins directory (in contrast with both run and include which defaults to using the EViews default directory). Thus, the command exec myprog1.prg will run the program file “Myprog1.prg” located in the default Add-ins directory. You may specify files using relative paths in the standard fashion. The command: exec MyAddInmyprog2.prg runs the program “Myprog2.prg” located in the “MyAddIn” subdirectory of the Add- ins directory. If you wish to run a program that is located in the same directory as the calling program, simply issue a “.” at the start of the program name: exec .myprog2.prg Alternatively you may use the include keyword to include the contents of a program file in another program file. For example, you can place the line include powers
- 156. Subroutines—149 at the top of any other program that needs to use the commands in POWERS. include also accepts a full path to the program file, and you may have more than one include statement in a program. For example, the lines, include c:programspowers.prg include durbin_h [more lines] will first execute all of the commands in “C:ProgramsPowers.PRG”, will execute the com- mands in “Durbin_h.PRG”, and then will execute the remaining lines in the program file. If you do not provide an absolute path for your include file, EViews will use the location of the executing program file as the base location. In the example above, EViews will search for the “Durbin_h.PRG” file in the directory containing the executing program. Note that in contrast to using exec to execute another program, include simply inserts the child program into the parent. This insertion is done prior to executing any lines in either program. One important consequence of this behavior is that any program variables that are declared in the parent program will not be available in the child/included program, even if they are declared prior to the include statement. Subroutines A subroutine is a collection of commands that allows you to perform a given task repeatedly, with minor variations, without actually duplicating the commands. You may also use sub- routines from one program to perform the same task in other programs. Defining Subroutines A subroutine begins with the keyword subroutine followed by the name of the routine and any arguments, and ends with the keyword endsub. Any number of commands can appear in between. The simplest type of subroutine has the following form: subroutine z_square series x = z^2 endsub where the keyword subroutine is followed only by the name of the routine. This subrou- tine has no arguments so that it will behave identically every time it is used. It forms the square of the existing series Z and stores it in the new series X. You may use the return command to force EViews to exit from the subroutine at any time. A common use of return is to exit from the subroutine if an unanticipated error is detected. The following program exits the subroutine if Durbin’s statistic (Greene, 2008, p. 646, or Davidson and MacKinnon, 1993, p. 360) for testing serial correlation with a lagged depen- dent variable cannot be computed: h
- 157. 150—Chapter 6. EViews Programming subroutine durbin_h equation eqn.ls cs c cs(-1) inc scalar test=1-eqn.@regobs*eqn.@cov(2,2) ' an error is indicated by test being nonpositive ' exit on error if test<=0 then return endif ' compute h statistic if test positive scalar h=(1-eqn.@dw/2)*sqr(eqn.@regobs/test) endsub Subroutine with Arguments The subroutines we have seen thus far have been written to work with a specific set of vari- ables. More generally, subroutines can take arguments. Arguments allow you to change the behavior of the group of commands each time the subroutine is used. You may be familiar with the concept from other programming languages, but if now, you are probably familiar with similar concepts in mathematics. You can define a function, say (6.1) where depends upon the argument . The argument is merely a place holder—it’s there to define the function and it does not really stand for anything. Then, if you want to evaluate the function at a particular numerical value, say 0.7839, you can write . If you want to evaluate the function at a different value, say 0.50123, you merely write . By defining the function, you save yourself from writing the full function expression every time you wish to evaluate it for a different value. To define a subroutine with arguments, you start with the subroutine keyword, followed by the subroutine name and (with no space) the arguments separated by commas, enclosed in parentheses. Each argument is specified by listing a type of EViews object, followed by the name of the argument. For example: subroutine power(series v, series y, scalar p) v = y^p endsub This subroutine generalizes the example subroutine Z_SQUARE. Calling the subroutine POWER will fill the series given by the argument V with the power P of the series specified by the argument Y. So if you set V equal to X, Y equal to Z, and P equal to 2, you will get the equivalent of the subroutine Z_SQUARE above. See the discussion below on how to call sub- routines. f x x 2 f x x f 0.7839 f 0.50123
- 158. Subroutines—151 When creating subroutines with scalar or string arguments, you will define your arguments using the scalar or the string types. Beyond that, you have a choice of whether you can to make the corresponding argument a (temporary) program variable or a (permanent) workfile object: • To make the argument a program variable, you should use a program variable name (beginning with a “!” for a control variable and a “%” for a string variable). If you choose to use program variables, they should be referred to using the “!” or “%” name inside the subroutine. • To make the argument a workfile object, you should use a standard EViews object name. The object should be referred to by the argument name inside the subroutine. Obviously, you can mix the two approaches in the definition of any subroutine. For example, the declaration subroutine mysub(scalar !a, string %b) uses program variable names, while subroutine mysub(scalar a, string b) uses object names. In the first case you should refer to “!A” and “%B” inside the subroutine; in the latter case, you should refer to the objects named “A” and “B”. If you define your subroutine using program variables, the subroutine will operate on them as though they were any other program variable. The variables, which cannot go out-of- scope, should be referred to using the “!” or “%” argument name inside the subroutine. If you define your subroutine using object names, the subroutine will operate on those vari- ables as though they were scalar or string objects. The variables, which may be deleted and may go out-of-scope (if, for example, you change the workfile page), should be referred to using the argument names as though they were scalar or string objects. (We discuss in detail related issues in “Calling Subroutines,” beginning on page 151.) You should note that EViews makes no distinction between input or output arguments in a subroutine. Each argument may be an input to the subroutine, an output from the subrou- tine, or both (or neither!). There is no way to declare that some arguments are inputs and some are outputs. There is no need to order the arguments in any particular order. However we find it much easier to read subroutine code when we stick to a convention, such as list- ing all output arguments prior to all input arguments (or vice versa). Calling Subroutines Once a subroutine is defined, you may execute the commands in the subroutine by using the call keyword. call should be followed by the name of the subroutine, and a list of any argument values you wish to use, enclosed in parentheses and separated by commas (with
- 159. 152—Chapter 6. EViews Programming no space after the subroutine name). If the subroutine takes arguments, the arguments must be provided in the same order as in the declaration statement. Here is an example program file that calls subroutines: include powers load mywork fetch z gdp series x series gdp2 series gdp3 call z_square call power(gdp2,gdp,2) call power(gdp3,gdp,3) The call of the Z_SQUARE subroutine fills the series X with the value of Z squared. Next, the call to the POWER subroutine creates the series GDP2 which is GDP squared. The last call to POWER creates the series GDP3 as the cube of GDP. When calling your subroutine, bear in mind that: • When the subroutine argument is a scalar, the subroutine may be called with a scalar object, a control variable, a simple number (such as “10” or “15.3”), a matrix element (such as “mat1(1,2)”) or a scalar expression (such as “!y+25”). • Subroutines with a string argument may be called with a string object, a string pro- gram variable, simple text (such as “hello”) or an element of an svector object. • Subroutines that take matrix and vector arguments can be called with a matrix name, and if not modified by the subroutine, may also take a matrix expression. • All other arguments must be passed to the subroutine with an object name referring to a single object of the correct type. In “Subroutine with Arguments” on page 150 we described how you can define subroutines that use either program variables or objects for scalar or string arguments. However you define your subroutine, you may call the subroutine using either program variables or objects—you are not required to match the calling arguments with the subroutine definition. Suppose, for example, that you define your subroutine as subroutine mysub(scalar a, string b) Then for scalar and string objects F and G, and program variables !X and %Y, scalar f = 3 string g = "hello" !x = 2 %y = "goodbye"
- 160. Subroutines—153 you may call the subroutine using any of the following commands: call mysub(!x, %y) call mysub(!x, g) call mysub(f, %y) call mysub(f, g) Note that changes to the scalars A and B inside the subroutine will change the correspond- ing program variable or object that you pass into the routine. Similarly, you may define subroutine mysub(scalar !a, string !b) and use the same four call statements to execute the subroutine commands. However the subroutine is called, bear in mind that behavior inside the subroutine is depen- dent on whether the subroutine declaration is in terms of program variables or objects, not on the variable type that is passed into the subroutine. Subroutine Placement Subroutine definitions may be placed anywhere throughout your program. However, for clarity, we recommend grouping subroutine definitions together either at the start or at the end of your program. The subroutines will not be executed until they are executed by the program using a call statement. For example: subroutine z_square series x=z^2 endsub ' start of program execution load mywork fetch z call z_square Execution of this program begins with the load statement; the subroutine definition is skipped and is executed only at the last line when it is “called.” Subroutine definitions must not overlap—after the subroutine keyword, there should be an endsub before the next subroutine declaration. Subroutines may call each other, or even call themselves. Alternatively, you may place frequently used subroutines in a separate program file and use an include statement to insert them at the beginning of your program. If, for example, you put the subroutine lines in the file “Powers.PRG”, then you may put the line: include powers
- 161. 154—Chapter 6. EViews Programming at the top of any other program that needs to call Z_SQUARE or POWER. You can use the subroutines in these programs as though they were built-in parts of the EViews program- ming language. Global and Local Variables Subroutines work with variables and objects that are either global or local. Global variables refer either to objects which exist in the workfile when the subroutine is called, and to objects that are created in the workfile by a subroutine. Global variables remain in the workfile when the subroutine finishes. A local variable is one that is defined within the subroutine. Local variables are deleted from the workfile once a subroutine finishes. The program that calls the subroutine will not be able to use a local variable since the local variable disappears once the subroutine finishes and the original program continues. Global Subroutines By default, subroutines in EViews are global. Global subroutine may refer to any global object that exists in the workfile at the time the subroutine is called. Thus, if Z is a series in the workfile, the subroutine may refer to and, if desired, alter the series Z. Similarly, if Y is a global matrix that has been created by another subroutine, the current subroutine may use the matrix Y. The rules for variables in global subroutines are: • All objects created by a global subroutine are global and will remain in the workfile when the subroutine finishes. • Global objects may be used and updated directly from within the subroutine. If, how- ever, a global object has the same name as an argument in a subroutine, the variable name will refer to the argument and not to the global variable. • The global objects corresponding to arguments may be used and updated by referring to the arguments. Here is a simple program that calls a global subroutine: subroutine z_square series x = z^2 endsub load mywork fetch z call z_square
- 162. Subroutines—155 Z_SQUARE is a global subroutine which has access to the global series Z. The new global series X contains the square of the series Z. Both X and Z remain in the workfile when Z_SQUARE is finished. If one of the arguments of the subroutine has the same name as a global variable, the argu- ment name takes precedence so that any reference to the name in the subroutine will refer to the argument, not to the global variable. For example: subroutine sqseries(series z, string sername) series {sername} = z^2 endsub load mywork fetch z fetch y call sqseries(y, "y2") In this example, there is a series Z in the original workfile and Z is also an argument of the subroutine. Calling SQSERIES with the argument set to Y tells EViews to use the series passed-in via the argument Z instead of the global Z series. On completion of the routine, the new series Y2 will contain the square of the series Y, not the square of the series Z. Since keeping track of variables can become confusing when subroutine arguments take the same name as existing workfile objects, we encourage you to name subroutine arguments as clearly and distinctly as possible. Global subroutines may call global subroutines. You should make certain to pass along required arguments when you call a subroutine from within a subroutine. For example, subroutine wgtols(series y, series wt) equation eq1 call ols(eq1, y) equation eq2 series temp = y/sqr(wt) call ols(eq2, temp) delete temp endsub subroutine ols(equation eq, series y) eq.ls y c y(-1) y(-1)^2 y(-1)^3 endsub can be run by the program: load mywork fetch cpi fetch cs
- 163. 156—Chapter 6. EViews Programming call wgtols(cs,cpi) In this example, the subroutine WGTOLS must explicitly pass along arguments to the sub- routine OLS so that it uses the correct series and equation objects. You cannot use a subroutine to change the object type of a global variable. Suppose that we wish to declare new matrices X and Y by using a subroutine NEWXY. In this example, the declaration of matrix X generates an error since X exists and is a series, but the declaration of the matrix Y works (assuming there is no Y in the workfile MYWORK, or that Y exists and is already a matrix): subroutine newxy matrix(2,2) x = 0 matrix(2,2) y = 0 endsub load mywork series x call newxy If you call this subroutine, EViews will return an error indicating that the global series X already exists and is of a different type than a matrix. Local Subroutines If you include the word local in the definition of the subroutine, you create a local subrou- tine. Local subroutines are most useful when you wish to write a subroutine which creates many temporary objects that you do not want to keep. The rules for variables in local subroutines are: • All objects created by a local subroutine will be local and will be removed from the workfile upon exit from the subroutine. • The global objects corresponding to subroutine arguments may be used and updated in the subroutine by referring to the arguments. • You may not use or update global objects that do not correspond to subroutine argu- ments. There is one exception to the general inaccessibility of non-argument global variables in local subroutines. When a global group is passed as an argument to a local subroutine, all of the series in the group are accessible to the local routine. The last two rules deserve a bit of elaboration. In general, local subroutines do not have access to any global variables unless those variables are associated with arguments passed into the subroutine. Thus, if there is a series X in the workfile, a local subroutine will not be
- 164. Subroutines—157 allowed to use or modify X unless it is passed into the subroutine using a series argument. Conversely, if X is passed into the subroutine, it may be modified. Since locally created objects will vanish upon completion of the subroutine, to save results from a local subroutine, you have to include them as arguments. For example, consider the subroutine: subroutine local ols_local(series y, series res, scalar ssr) equation temp_eq.ls y c y(-1) y(-1)^2 y(-1)^3 temp_eq.makeresid res ssr = temp_eq.@ssr scalar se = ssr/temp_eq.@df endsub This local subroutine takes the series Y as input and modifies the argument series RES and argument scalar SSR as output. Note that since Y, RES, and SSR are the only arguments of this local subroutine, the only global variables that may be used or modified are those asso- ciated with these arguments.The equation object TEMP_EQ and the scalar SE are local to the subroutine and will vanish when the subroutine finishes. Here is an example program that calls this local subroutine: load mywork fetch hsf equation eq1.ls hsf c hsf(-1) eq1.makeresid rres scalar rssr = eq1.@ssr series ures scalar ussr call ols_local(hsf, ures, ussr) Note that we first declare the series URES and scalar USSR before calling the local subrou- tine. These objects are global since they are declared outside the local subroutine. Since we call the local subroutine by passing these global objects as arguments, the subroutine can use and update these global variables. Object commands that require access to global variables should be used with care in local subroutines since that the lack of access to global variables can create problems for views or procs of objects passed into the routine. For example, a subroutine of the form: subroutine local bg(equation eq) eq.hettest z c endsub
- 165. 158—Chapter 6. EViews Programming will fail because the hettest equation proc requires access to the original variables in the equation and the global variable Z, and these series are not available since they are not passed in as arguments to the subroutine. Care should also be taken when using samples and local subroutines. If the workfile sample is based upon a series in the workfile (for example “smpl @all if x>0”), most procedures inside the local subroutine will fail unless all of the series used in the sample are passed into the subroutine. Local Samples Local samples in subroutines allow you to indicate that changes to the workfile sample are temporary, with the original sample restored when you exit the routine. This feature is use- ful when designing subroutines which require working on a subset of observations in the original sample. You may, in a subroutine, use the local smpl statement to indicate that subsequent changes to the sample are temporary, and should be undone when exiting the subroutine. The command local smpl makes a copy of the existing sample specification. You may then change the sample as many times as desired using the smpl statement, and the original sample specification will be reapplied when existing from the subroutine. You may use the global smpl statement to indicate that subsequent smpl statements will result in permanent changes to the workfile sample. Thus, the commands: global smpl smpl 5 100 in a subroutine permanently change the sample. For example, consider the following program snippet which illustrates the behavior of local and global samples: workfile temp u 100 call foo subroutine foo smpl 2 100 local smpl smpl 10 100 endsub Here, we create a workfile with 100 observations and an initial workfile sample of “1 100”, then call the subroutine FOO. Inside FOO, the first smpl statement changes the workfile
- 166. User-Defined Dialogs—159 sample to “2 100”. We then issue the local smpl statement which backs up the existing sample and identifies subsequent sample changes as local. The subsequent change to the “10 100” sample is local so that when the subroutine exits, the sample is reset to “2 100”. If instead we define FOO to be subroutine foo smpl 2 100 local smpl smpl 10 100 global smpl smpl 5 100 local smpl smpl 50 100 endsub As before, first smpl statement changes the workfile sample to “2 100” and the local smpl statement and following smpl statement set the local sample to “10 100”. The global smpl indicates that subsequent sample statements will once again be global so the next line per- manently changes the workfile sample to “5 100”. Note that the last local smpl and subse- quent smpl statement change the local sample only. When we exit the subroutine the sample will be set to the last global sample of “5 100”. User-Defined Dialogs EViews offers the ability to construct several types of user-interface controls, or dialogs, within your program. These dialogs permit users to input variables or set options during the running of the program, and allow you to pass information back to users. There are five different functions that create dialogs in EViews: • @uiprompt (p. 733) – creates a prompt control, which displays a message to the user. • @uiedit (p. 729) – creates an edit control, which lets users input text. • @uilist (p. 731) – creates a list control, which lets users select from a list of choices. • @uiradio (p. 734) – creates a set of radio controls, which lets users select from a set of choices. • @uidialog (p. 726) – creates a dialog which contains a mixture of other controls. • @uifiledlg (p. 730) – creates a open/save-style dialog to obtain the name of a file. Each dialog function returns an integer indicating how the user exited the dialog:
- 167. 160—Chapter 6. EViews Programming Note that the only dialog types that can return exit conditions other than “Cancel” or “OK” are @uiprompt and @uidialog. If “Cancel” is pressed, the variables passed into the dialog will not be updated with whatever settings the user has chosen. If “OK” is pressed, then the dialog changes are accepted. Each of the dialog functions accept arguments that are used to define what will be displayed by the dialog, and that will be used to store the user's inputs to the dialog. You may use string or a scalar arguments, where both the string and scalar can be a literal, a program variable, or a workfile object. @uiprompt The @uiprompt(string prompt, string type) function creates a simple message/prompt box that displays a single piece of text, specified using the prompt argument, and lets the user click a button to indicate an action. The choices of action offered the user will depend upon the string specified in type. • if type is equal to “O”, or is omitted completely, then the dialog will only have an “OK” button. This type of prompt dialog would typically be used to provide a message to the user. • if type is equal to “OC”, then the dialog will have an “OK” button and a “Cancel” but- ton. This type of prompt dialog would be used to pass a message onto the user and let them continue or cancel from the procedure. • if type is equal to “YN”, then the dialog will contain a “Yes” button and a “No” button which can be used to ask the user a question requiring an affirmative or negative response. • if type is equal to “YNC” the dialog will have a “Yes” button, a “No” button and a “Cancel” button. For example, the command: @uiprompt("Welcome to EViews") User Selection Return Value Cancel -1 OK 0 Yes 1 No 2
- 168. User-Defined Dialogs—161 will display a simple dialog box with a simple welcome mes- sage and an “OK” button, while the command @uiprompt("Would you like to continue", "YN") displays a dialog asking the user if they would like to continue the program, with a “Yes” or “No” answer. Note that the arguments to the function may be a program variable or string object rather than string literals. The following sets of commands give identical results to that above: %type = "YN" %msg = "Would you like to continue" scalar ret = @uiprompt(%msg, %type) The return value of the control is determined by the user response: Cancel (-1), OK (0), Yes (1), No (2). See @uiprompt (p. 733) for additional detail. @uiedit The @uiedit(string IOString, string prompt, scalar maxEditLen) function provides an edit control that lets the user enter text which is then stored in the string IOString. If IOString contains text before the @uiedit function is called, the edit box will be initialized with that text. The string prompt is used to specify text to be displayed above the edit box, which may be used to provide a message or instructions to the user. maxEditLen is an option integer scalar that specifies the maximum length that IOString can take. If the user tries to enter text longer than maxEditLen characters, the extra characters will be truncated. By default maxEditLen is set to 32. Both prompt and maxEditLen may be written in-line using string literals, but IOString must be either a program variable or a string object in your workfile. As an example, %eqname = "eq_unemp" scalar ret = @uiedit(%eqname, "Enter a name for your equation") equation {%eqname} unemp c gdp gdp(-1) will display the following dialog box and then create an equation object with a name equal to whatever was entered for %EQNAME.
- 169. 162—Chapter 6. EViews Programming The return value of the control is determined by the user response: Cancel (-1) or OK (0). See @uiedit (p. 729) for additional detail. @uilist This function creates a list box dialog, which lets the user select one item from a list. There are two forms of the @uilist function, one that returns the user's selection as a string IOString, @uilist(string IOString, string prompt, string list) and one that stores it as an integer IOScalar representing the position of the selection in the list, @uilist(scalar IOScalar, string prompt, string list) The string prompt is used to specify text to be displayed above the list box, providing a mes- sage or instructions to the user. The string list is a space delimited list of items that will be displayed in the list box. To spec- ify entries with spaces, you should enclose the entry in double-quotes using double-quote escape characters. Both prompt and list may be provided using in-line text, but IOString or IOScalar must be either a program variable or an object in your workfile. If the IO variable (IOString or IOScalar) is defined before the function is called, then the list box control will have the item defined by the variable pre-selected. If the IO variable does not match an item in the list box, or if it is not pre-defined, the first item in the list will be selected. The following program lines provide the user with a choice of robust standard errors, and then displays that choice on the statusline: %choice = "White" %list = "Default White HAC" scalar ret = @uilist(%choice, "Standard Errors Choice", %list) statusline %list
- 170. User-Defined Dialogs—163 Note that the above program could also be run with the following lines: !choice = 2 %list = "Default White HAC" scalar ret = @uilist(!choice, "Standard Errors Choice", %list) %choice = @word(%list,!choice) statusline %choice The return value of the control is determined by the user response: Cancel (-1) or OK (0). See @uilist (p. 731) for details. @uimlist This function is similar to @uilist in that it creates a list box dialog, with a difference being that here multiple selections from the list may be made. The form of the @uimlist function is: @uimlist(vector IOVector, string prompt, string list) The string prompt is used to specify text to be displayed above the list box, providing a mes- sage or instructions to the user. The string list is a space delimited list of items that will be displayed in the list box. To spec- ify entries with spaces, you should enclose the entry in double-quotes using double-quote escape characters. Both prompt and list may be provided using in-line text, but IOString or IOScalar must be either a program variable or an object in your workfile. If the IO variable (IOVector) is defined before the function is called, then the list box control will have the items defined by the vector pre-selected. If the IO variable does not match an item in the list box, or if it is not pre-defined, the first item in the list will be selected. The following program lines provide the user with a choice of robust standard errors, and then displays those choices on the statusline: vector(1) choice = 2 %list = "Default White HAC" scalar ret = @uimlist(choice, "Standard Errors Choice", %list)
- 171. 164—Chapter 6. EViews Programming statusline %list @uiradio @uiradio(scalar IOScalar, string prompt, string list) is similar to @uilist in that it pro- vides a dialog that lets the user select from a list of choices. However rather than selecting an item from a list box, the user must select the desired item using radio buttons. The @uiradio function will return the user's choice in IOScalar. The string prompt should be used to specify a message or instructions to the user to be dis- played above the set of radio buttons. The string list is a space delimited list of items that contains the items for the radio buttons. To specify entries with spaces, you should enclose the entry in double-quotes using double- quote escape characters. Both prompt and list may be specified using in-line text, but IOScalar must be either a pro- gram variable or an object in your workfile. If IOScalar is defined and set equal to a valid integer before the function is called, the radio button associated with that integer will be the default selection when the dialog is shown. If IOScalar is not a valid choice, the first radio will be selected. As an example, we replicate the standard error choice example from the @uilist function, but this time use radio buttons: !choice = 2 %list = "Default White HAC" scalar ret = @uiradio(!choice, "Standard Errors Choice", %list) %choice = @word(%list,!choice) statusline %choice
- 172. User-Defined Dialogs—165 The return value of the control is determined by the user response: Cancel (-1) or OK (0). See @uiradio (p. 734) for additional detail. @uidialog The @uidialog(control_spec1[, control_spec2, ….]) function displays a dialog which may be composed of different controls, including simple text, edit boxes, list boxes, radio buttons and check boxes. The dialog is specified using a list of control specifications passed into the function as arguments. Each control specification is a type keyword specifying the type of control, followed by a list of parameters for that control. The type keywords should be from the following list: The “edit”, “list” and “radio” controls are similar to their individual dialog functions, and the specifications for those controls follow the same pattern. Thus the specification for an edit box would be (“edit”, string IOString, string prompt, scalar maxEditLen). The “caption” control changes the title of the dialog, shown in the title bar. The caption keyword should be followed by a string containing the text to be used as the caption, yield- ing a specification of (“caption”, string caption). Keyword Control “caption” Dialog title “text” Plain text “edit” Edit box “list” List box “radio” Radio buttons “check” Check box “button” OK-type button “buttonc” Cancel-type button “colbreak” Column break “setoc” Set OK/Cancel text
- 173. 166—Chapter 6. EViews Programming The “text” control adds basic text to the dialog. Like the caption control, the text control keyword, “text”, should be followed by a string containing the text to be used, yielding a specification of (“text”, string text). The “check box” control adds a check box to the dialog. The check keyword should be fol- lowed by a scalar, IOScalar, which stores the selection of the check box - 1 for checked, and 0 for unchecked, and then by a string prompt which contains the text to be used as a prompt/instructions for the check box. The specification for the check box control is then: (“check”, scalar IOScalar, string prompt). The “button” and “buttonc” controls add a custom button to the dialog. The dialog will close after a button has been pressed. The behavior of the button will depend on the type of button —buttons of type “button” will behave in the same way as the “OK” button (i.e., all variables passed into the dialog will be updated to reflect changes made to their correspond- ing controls). Buttons of type “buttonc” will behave in the same way as the “Cancel” button (i.e., all variables will be reset to the values that were passed into the dialog). The return value of the dialog will correspond to the order in which buttons are placed in the dialog. If only one button (apart from the standard “OK” and “Cancel”) is included in the dialog, the return value for that button will be “1”. If there is more than one button, then the first button will return a value of “1”, the second will return a value of “2” and so on. Note that the return value is independent of whether the button was of type “button” or “buttonc”. The specification for the button controls is (“button[c]”, “text”) where text speci- fies the text that will be on the button. The column break control inserts a column break. By default, EViews will automatically choose the number of columns in the constructed dialog. There is still a maximum of only two columns allowed, but by adding a “colbreak” control, you can force the position of a break in the dialog. “setoc” allows you to change the text of the “OK” and “Cancel” buttons on the dialog. You should supply two words, separated by a space as the text for “OK” and “Cancel”. As an example, a dialog that offers a choice of covariance matrix options, plus a check box for degree of freedom adjustment, could be made with: !choice = 2 !doDF = 1 %list = "Default White HAC" scalar ret = @uidialog("caption", "Covariance Options", "list", !choice, "Covariance method", %list, "check", !doDF, "DF- adjust")
- 174. User-Defined Dialogs—167 See @uidialog (p. 726) for details. @uifiledlg The @uifiledlg(string IO_Filespec, string filter, string style) displays a standard Windows file open or save dialog so that you may obtain the path and name of a file. @uifiledlg function will return the user's specification in file_spec. The string IO_Filespec should be used to specify an initial location and possibly the name for the file. IO_Filespec will also contain the file specified on return. The string filter is used to specify the types of files to show in the dialog using the filter argu- ment, with, for example, “” used to denote all files, and “prg” used to limit the display to files ending in “.prg”. The type argument is used to determine whether the shown dialog has an “open” or a “save” prompt. (Note that the clicking OK on the dialog does not actually open or save the selected file, it merely returns the name of the selected file. Thus, specifying the type argument is for cos- metic reasons only.) Both filter and style may be specified using in-line text, but IO_Filespec must be either a pro- gram variable or an object in your workfile. The displayed dialog will display both an OK and a Cancel button, and will return an inte- ger representing the button clicked: Cancel (-1), OK (0). string myfile = "c:temp" @uifiledlg(myfile, "prg”, "open") These commands display a file open dialog style containing a list of all files with a “.prg” extension in the folder “c:temp”. The user can navigate through the file system and select another file, whose path and name will be returned in the string MYFILE. Note that the slightly different set of commands string myfile = "c:temp" @uifiledlg(myfile, "prg", "save")
- 175. 168—Chapter 6. EViews Programming will instead display a save dialog that opens in the “c:” folder with the filename initialized to “temp.prg” (MYFILE does not have the trailing “”). See also @uifiledlg (p. 730). Example Program This program creates a workfile and some series, then puts up dialogs asking the user to specify the dependent variable and the regressors to be used in a least-squares equation. Note that the part of the example program that generates the data could be replaced with a command to open up an existing workfile. 'Create a workfile and some series wfcreate temp u 100 series y=nrnd series x=nrnd series w=nrnd '------------------------------------- 'variable to store name of dependent variable %dep = "" 'variable to store list of regressors. Default is "c" for a con- stant. %regs = "c " 'variable that will track the result of dialogs. -1 indicates user hit Cancel. 0 Indicates user hit OK. !result = 0 'put up an Edit dialog asking for dependent variable. !result = @uiedit(%dep,"Enter the dependent variable") if !result=-1 then 'if user cancelled, then stop program stop endif 'put up an Edit dialog asking for regressors list. !result = @uiedit(%regs,"Enter the list of regressors") if !result=-1 then 'if user cancelled, then stop program stop endif equation eq1.ls {%dep} {%regs} 'estimate equation. Three program variables are used in this program: %DEP, %REGS and !RESULT. %DEP is the string variable that will contain the user’s entry for the dependent variable. To begin, we set this equal to an empty string. %REGS is used to store the user’s entry for the list of
- 176. Version 4 Compatibility Notes—169 regressors. We set this equal to “C” to begin with. This means that the default setting for the regressor list will be a constant. !RESULT will be used to track the completion of the dialogs. Every dialog returns an integer value depending on whether the user clicked OK or Cancel (or in some cases Yes or No). We initialize this value to 0. Following the declaration of the variables, the first dia- log is brought up using the @uiedit command. This will create an Edit dialog asking the user to “Enter the dependent variable.” The user’s response will be stored in %DEP. Following the dialog command, we check whether the value of !RESULT is equal to -1. A -1 indicates that the user pressed Cancel in the dialog. If this is the case, the program quits, using the stop command. The second dialog command is similar to the first, but rather than asking for the dependent variable, it asks the user to “Enter the list of regressors,” and stores that list in %REGS. Note that since %REGS was set equal to “C” prior to the dialog being put up, the dialog will be pre-filled with the constant term. Users can still delete the constant or add extra regressors. Finally, having obtained entries for both %DEP and %REGS, equation EQ1 is estimated via least squares with the specified variables. Version 4 Compatibility Notes While the underlying concepts behind strings, string variables, and replacement variables have not changed since the first version of EViews, there were three important changes in the implementation of these concepts introduced in EViews 5. In addition, there has been an important change in the handling of boolean comparisons involving numeric NA values, and blank string values. String vs. Replacement Variables First, the use of contextual information to distinguish between the use of string and replace- ment variables has been eliminated. Prior to EViews 5, the underlying notion that the expression “%X” refers exclusively to the string variable %X while the expression “{%X}” refers to the corresponding replacement variable was modified slightly to account for the context in which the expression was used. In particular, the string variable expression “%X” was treated as a string variable in cases where a string was expected, but was treated as a replacement variable in other settings. For example, suppose that we have the string variables: %y = "cons" %x = "income"
- 177. 170—Chapter 6. EViews Programming When used in settings where a string is expected, all versions of EViews treat %X and %Y as string variables. Thus, in table assignment, the command, table1(2, 3) = %x + " " + %y is equivalent to the expression, table1(2, 3) = "cons" + " " + "income" However, when string variables were used in other settings, early versions of EViews used the context to determine that the string variable should be treated as a replacement variable; for example, the three commands equation eq1.ls %y c %x equation eq1.ls {%y} c {%x} equation eq1.ls cons c income were all viewed as equivalent. Strictly speaking, the first command should have generated an error since string variable substitution would replace %Y with the double-quote delim- ited string “cons” and %X with the string “income”, as in equation eq1.ls "cons" c "income" Instead, EViews determined that the only valid interpretation of %Y and %X in the first command was as replacement variables so EViews simply substituted the names for %Y and %X. Similarly, the commands, series %y = %x series {%y} = {%x} series cons = income all yielded the same result, since %Y and %X were treated as replacement variables in the first line, not as string variables. The contextual interpretation of string variables was convenient since, as seen from the examples above, it meant that users rarely needed to use braces around string variables. The EViews 5 introduction of alphanumeric series meant, however, that the existing interpreta- tion of string variables was no longer tenable. The following example clearly shows the problem: alpha parent = "mother" %x = "parent" alpha temp = %x Note that in the final assignment statement, the command context alone is not sufficient to determine whether %X should refer to the string variable value “parent” or to the replace- ment variable PARENT, which is an Alpha series containing the string “mother”.
- 178. Version 4 Compatibility Notes—171 Consequently, in EViews 5 and later, users must now always use the expression “{%X}” to refer to the replacement variable corresponding to the value of %X. Thus, under the new interpretation, the final line in the example above resolves to alpha temp = "parent" Under the EViews 4 interpretation of the final line, “%X” would have been treated as a replacement variable so that TEMP would contain the value “mother”. To interpret the last line as a replacement variable in EViews 5 and later, you must now explicitly provide braces around the string variable alpha temp = {%x} to resolve to the command alpha temp = parent String Variables in String Expressions The second major change in EViews 5 is that text in a string expression is now treated as a literal string. The important implication of this rule is that string variable text is no longer substituted for inside a string expression. Consider the assignment statements %b = "mom!" %a = "hi %b" table(1, 2) = %a In EViews 4 and earlier, the “%B” text in the string expression was treated as a string vari- able, not as literal text. Accordingly, the EViews 4 string variable %A contains the text “hi mom!”. One consequence of this approach is that there was no way to get the literal text of the form “%B” into a string using a program in EViews 4. Beginning in EViews 5, the “%B” in the second string variable assignment is treated as lit- eral text. The string variable %A will contain the text “hi %b”. Obtaining a %A that con- tains the EViews 4 result is straightforward. Simply move the first string variable %B outside of the string expression, and use the string concatenation operator: %a = "hi " + %b assigns the text “hi mom!” to the string variable %A. This expression yields identical results in all versions of EViews. Case-Sensitive String Comparison In early versions of EViews, program statements could involve string comparisons. For example, you might use an if-statement to compare a string variable to a specific value, or you could use a string comparison to assign a boolean value to a cell in a matrix or to a
- 179. 172—Chapter 6. EViews Programming numeric series. In all of these settings, the string comparisons were performed caselessly, so that the string “Abc” was viewed as equal to “ABC” and “abc”. The introduction of mixed case alpha series in EViews 5 meant that caseless string compari- sons could no longer be supported. Accordingly, the behavior has been changed so that all EViews 5 and later string comparisons are case-sensitive. If you wish to perform caseless comparison in newer versions of EViews, you can use the @upper or @lower string functions, as in if (@lower(%x) = "abc") then or if (@upper(%x) = "ABC") then Alternately, programs may be run in version 4 compatibility mode to enable caseless com- parisons for element operations (see “Version 4 Compatibility Mode” on page 173). For example, the if-statement comparison: if (%x = "abc") then will be performed caselessly in compatibility mode. Note that compatibility mode does not apply to string comparisons that assign values into an entire EViews series. Thus, even in compatibility mode, the statement: series y = (alphaser = "abc") will be evaluated using case-sensitive comparisons for each value of ALPHASER. Comparisons Involving NAs/Missing Values Prior to EViews 5, NA values were always treated as ordinary values for purposes of numeric equality (“=”) and inequality (“<>”) testing. In addition, when performing string comparisons in earlier versions of EViews, empty strings were treated as ordinary blank strings and not as a missing value. In these versions of EViews, the comparison operators (“=” and “<>”) always returned a 0 or a 1. In EViews 5 and later, the behavior of numeric and string inequality comparisons involving NA values or blank strings has been changed so that comparisons involving two variables propagate missing values. To support the earlier behavior, the @eqna and @neqna functions are provided so that users may perform comparisons without propagating missing values. Complete details on these rules are provided in “String Relational Operators” on page 79 of the Command and Programming Reference and in “Numeric Relational Operators” on page 172 of User’s Guide I. Programs may be run in version 4 compatibility mode to enable the earlier behavior of com- parisons for element operations. For example, the if-statement comparison:
- 180. References—173 if (!x = !z) then will not propagate NA values in compatibility mode. Note that compatibility mode does not apply to comparisons that assign values into an EViews numeric or alpha series. Thus, even in compatibility mode, the statement: series y = (x = z) will propagate NA values from either X or Z into Y. Version 4 Compatibility Mode While the changes to the handling of string variables and element boolean comparisons are required for extending the programming language to handle the new features of the EViews 5 and later, we recognize that users may have a large library of existing programs which make use of the previous behavior. Accordingly, EViews provides a version 4 compatibility mode in which you may run EViews programs using the previous context sensitive handling of string and substitution variables, the earlier rules for resolving string variables in string expressions, and the rules for caseless string comparison and propagation of missing values in element comparisons. There are two ways to ensure that your program is run in version 4 compatibility mode. First, you may specify version 4 compatibility mode at the time the program is run. Compat- ibility may be set interactively from the Run Program dialog (“Executing a Program” on page 120) by selecting the Version 4 compatible variable substitution and program bool- ean comparisons checkbox, or in a program using the “ver4” option (see run (p. 467)). Alternatively, you may include “MODE VER4” statement in your program. See “Program Modes” on page 134 for details. References Davidson, Russell and James G. MacKinnon (1993). Estimation and Inference in Econometrics, Oxford: Oxford University Press. Greene, William H. (2008). Econometric Analysis, 6th Edition, Upper Saddle River, NJ: Prentice-Hall.
- 181. 174—Chapter 6. EViews Programming
- 182. Chapter 7. External Connectivity EViews offers several methods for interacting with external applications and data: • The EViews OLEDB driver provides an easy-to-use interface for external programs to read data stored in EViews workfiles (WF1) and EViews databases (EDB) (“The OLEDB Driver” on page 176). • The EViews Excel Add-in offers a simple interface for reading data stored in EViews work- files and databases from within Microsoft Excel (“The Excel Add-in” on page 176). • The EViews Database Objects (EDO) Library gives you the ability to access data objects held inside EViews databases and workfiles from within an external application (“EViews Database Objects (EDO) Library” on page 176). • EViews offers COM Automation server support so that external programs or scripts can launch or control EViews, transfer data, and execute EViews commands (“EViews COM Automation Server” on page 177). • EViews offers COM Automation client support for MATLAB and R application servers so that EViews may be used to launch and control the application, transfer data, and exe- cute commands (“EViews COM Automation Client Support (MATLAB and R)” on page 177). • EViews offers an EViews Database Extension (EDX) interface for developers who wish to provide EViews access to their database formats. Any external data source that imple- ments this interface can be opened directly from within EViews Enterprise Edition and used just like an EViews database (“EViews Database Extension Interface” on page 184). Reading EViews Data EViews offers a variety of ways for you to access data stored in EViews workfiles (WF1) and EViews databases (EDB) from external sources. The EViews OLEDB driver provides an easy way for OLEDB-aware clients or custom programs to read EViews data Alternatively, the EViews Microsoft Excel Add-in allows users to fetch and link to EViews data located in workfiles and databases. The Add-in offers an easy-to-use interface to OLEDB for read- ing EViews data from within Excel. Lastly, you may use the EViews Database Objects Library to access EViews data from within an external application.
- 183. 176—Chapter 7. External Connectivity The OLEDB Driver The EViews OLEDB driver is automatically installed and registered on your computer when you install EViews. Once installed, you may use OLEDB-aware clients or custom programs to read series, vector, and matrix objects directly from EViews workfiles and databases. See “The OLEDB Driver” on page 158 of User’s Guide I for discussion. For additional details, see the Using the EViews OLEDB Driver whitepaper available from our website www.eviews.com/download/download.html. The Excel Add-in The EViews Excel Add-in offers a simple interface for fetching and linking to data stored in EViews workfiles and databases from within Microsoft Excel (2000 and later). See “The Excel Add-in” on page 155 of User’s Guide I for discussion. For additional details, see the Using the EViews OLEDB Driver whitepaper available from our website www.eviews.com/download/download.html. EViews Database Objects (EDO) Library The EViews Database Objects (EDO) Library is an EViews Enterprise Edition feature that provides access to data objects held inside EViews databases and workfiles from within an external application. The library consists of a set of COM objects exported by EViews that can easily be used in a variety of development environments such as Microsoft .NET and Visual Basic for Applica- tions (VBA). The EDO API provides full read and write access to EViews databases, as well as read-only access to EViews workfiles. The interface provides access to both the observation values and the attributes of an object. In contrast, the EViews OLEDB interface “The OLEDB Driver” on page 176 offers data reading support but not write operations nor object attributes. The EDO library supports reading and writing of the following EViews data objects: • Numeric series (including series containing dates) • Alpha series (containing character data) • Scalars • Vectors • Matrices • Strings • String Vectors
- 184. EViews COM Automation Client Support (MATLAB and R)—177 The library does not currently support access to data within structured EViews objects such as equations or models. Use of the EDO library requires that a machine is registered with a valid EViews Enterprise Version license. For additional details, see the EViews Data Objects Library whitepaper, available from our website www.eviews.com/download/download.html. EViews COM Automation Server EViews may be used as a COM Automation server so that an external program or script may launch and control EViews programmatically. EViews COM is comprised of two class objects: Manager and Application. The Manager class is used to manage and create instances of the main EViews Application class. The Application class provides access to EViews functionality and data. Most notably, the Application class Run and a variety of Get and Put methods provide you with access to EViews commands and allow you to obtain read or write access to series, vectors, matrix, and scalar objects. For a complete description of these methods, please refer to the EViews COM Automation Server whitepaper, available from our website www.eviews.com/download/download.html. Note that web server access to EViews via COM is not allowed. Furthermore, EViews will limit COM access to a single instance when run by other Windows services or run remotely via Distributed COM. EViews COM Automation Client Support (MATLAB and R) EViews offers COM Automation client support for select external application servers. Sup- port is currently provided for two applications: MATLAB and R. The client support includes a set of EViews functions for exporting EViews data to the exter- nal application, running commands and programs in the application, and importing data back into EViews. These functions provide easy access to the powerful programming lan- guages of MATLAB and R to create programs and routines that perform tasks not currently implemented in EViews. The interface also offers access to the large library of statistical rou- tines already written in the MATLAB and R languages. There are six EViews commands that control the use of external applications: xclose (p. 537), xget (p. 537), xlog (p. 540), xopen (p. 540), xput (p. 542), and xrun (p. 544). xopen and xclose are used to open and close a connection to the external application (MATLAB or R). xput and xget are used to send data to and from the external application.
- 185. 178—Chapter 7. External Connectivity xrun is used to send a command to the external application, and, finally, xlog lets you show or hide an external application log window within EViews. Using MATLAB® To open a connection to MATLAB, simply use the xopen(type=m) command. EViews will then attempt to launch MATLAB on your computer. Note, your computer must have access to MATLAB, either through a local installation, or through a network. EViews has been tested with MATLAB release R2008a, although other versions may work as well. Once a connection to MATLAB has been made, xput (p. 542) may be used to pass data from EViews over to MATLAB. All numerical data is passed to MATLAB as a matrix. String data (in the form of an alpha series or svector) will be passed to a MATLAB char if each string contains the same number of characters. Otherwise the string data will be passed as a cell array. Series and group objects are always filtered by the current sample unless you specify an explicit sample in the xput command. Note that object names in EViews are not case sensitive. Unless otherwise specified, EViews objects that are passed into MATLAB using xput will have the same name as the EViews objects that are being pushed, with the case determined by the case established for the COM connection (see xopen (p. 540)). xrun can be used to issue a single line command to MATLAB. You may, for example, use xrun to invert a matrix or run a program in MATLAB. If you wish to run multiple com- mands, each command must be entered with a separate xrun command. Commands should be surrounded in quotes. xget can be used to fetch data from MATLAB into EViews. The “type=” option lets you specify the type of object that will be created in EViews. If no option is specified, MATLAB matrices will be brought in as EViews matrix objects, while chars and cell arrays will be brought in as svectors. If you use “type=series” or “type=alpha” to specify that the data is brought in as a series or an alpha series, EViews will create a series of workfile length, and either truncate the data, or pad with NAs, if the incoming matrix does not have the same number of rows as there are workfile observations. The follow program offers a simple example using MATLAB to perform a least-squares regression (more complicated operations may be performed by using xrun to run a MATLAB program): 'create a workfile wfcreate u 100 'create some data series y=nrnd series x1=nrnd
- 186. EViews COM Automation Client Support (MATLAB and R)—179 series x2=nrnd 'group regressor data into a group group xs c x1 x2 'open a connection to Matlab with lower-case default output names xopen(type=m, case=lower) 'put regressors and dependent variable into Matlab xput xs xput y 'run a command to perform least squares as a matrix operation xrun "beta = inv(xs'*xs)*xs'*y" 'retrieve betas back into EViews xget beta 'perform same least squares estimation in EViews equation e1.ls y xs show e1 show beta 'close Matlab connection xclose The program first creates a new workfile, and then creates some series objects. The series called Y is the dependent variable, and the series X1 and X2 are the regressors (along with a constant). xopen is used to open a connection to MATLAB, and then xput is used to pass the dependent variable and the regressors over to MATLAB. Note that the names of the matrices and vectors will all be lower-cased in MATLAB since the connection was opened with the “case=lower” option. xrun is used to create a vector in MATLAB, called “beta”, equal to the least squares coeffi- cient, using the matrix algebra formula for LSQ. beta is brought back into EViews with the xget command, and then we estimate the same equation inside EViews and show the two results for comparison. Finally the connection to MATLAB is closed. Using R R is a GNU-project software environment for statistical computing and graphics. R is free software (under the terms of the GNU General Public License) that is readily available for download from the Official R Project website: http://www.r-project.org/ and other mirror sites. To use the EViews external interface to R, you must have R installed on your Windows com- puter, along with the rscproxy and statconnDCOM packages. Once installed, you may use the commands listed above to communicate with R from within EVIews.
- 187. 180—Chapter 7. External Connectivity Installing R Components EViews was developed and tested with the following R components. You must have the fol- lowing installed on the machine running EViews (or have R server components registered on the same machine via DCOM): • R, version 2.8.1 or later. • rscproxy, version 1.0-12. • statconnDCOM, version 3.1-1B2. Newer versions of rscproxy or statconnDCOM may work, but have not been tested with EViews. Note that rscproxy version 1.0-13 has known issues with the console output feature, such that you may not see any output in the R Output Log window in EViews. New R installation If you do not currently have R installed, the easiest procedure is to run the latest RAnd- Friends setup file located at http://rcom.univie.ac.at/download.html As of this documentation, RAndFriendsSetup2090V3.0-14-2.exe is the latest file, which includes the proper rscproxy version 1.0-12. Run the basic installation of RAndFriendsSet- up2090V3.0-14-2.exe, leaving the Direct internet connection box checked, to install R ver- sion 2.9.0, rscproxy version 1.0-12, and statconnDCOM version 3.1-1B2. After installing statconnDCOM, you may cancel out of the installation of RExcel, as it is not needed by EViews. Modifying an Existing R installation If you already have R installed, verify that it is at least version 2.8.1. Also, you should check your R installation write path and version information in your windows registry, as this information is needed by statconnDCOM to find your R installation. You should have an entry in the registry that looks like the following: Windows Registry Editor Version 5.00 [HKEY_LOCAL_MACHINESOFTWARER-core] [HKEY_LOCAL_MACHINESOFTWARER-coreR] "InstallPath"="C:Program FilesRR-2.8.1" "Current Version"="2.8.1" [HKEY_LOCAL_MACHINESOFTWARER-coreR2.8.1]
- 188. EViews COM Automation Client Support (MATLAB and R)—181 "InstallPath"="C:Program FilesRR-2.8.1" If you don’t have these entries in your registry, you can re-install R and check the checkbox Save version number in registry (this option is checked by default) or you can manually add them to the registry (with your proper values for “Install Path” and “Current Version”). If you choose to edit your registry, we highly recommend that you first make a backup copy. The current version of R is available at one of the Comprehensive R Archive Network sites near you (go to “http://cran.r-project.org/mirrors.html” for a current list). You should navi- gate to the “bin/windows/base” directory and download the installer for the current version (it should have a name that looks like “R-X.X.X-win32.exe”. For more info, see the R Win- dows FAQ, which currently may be found at http://cran.r-project.org/bin/windows/base/rw-FAQ.html Installing rscproxy and statconnDCOM When you have verified that the R installation path and version in your registry are correct, you can install rscproxy and statconnDCOM. To install the rscproxy package, open R and select the PackagesInstall Package(s)... menu item to see a list of available packages. First select your country and region, and then find rscproxy and click OK. To install statconnDCOM, download the lat- est version from http://rcom.univie.ac.at/download.html and run the setup program. Verifying installation Once installation is complete, run the statconn “Server 01 - Basic Test” program, usually found under Start->All Programs->statconn->DCOM->Server01 – Basic Test, to verify that everything is working. Click on the Start R button to begin the test.
- 189. 182—Chapter 7. External Connectivity Once you have run the test, you should be able to run EViews and use the xopen command to open a connec- tion to R. xopen(type=r) This command will open a connection to R from within EViews and will also open an R output log window in EViews. You may then use the xput, xrun, and xget com- mands to transfer data and run commands in R from within EViews. By default, EViews series, alpha, vector, and matrix objects transferred to R using xput will be written as R vectors. EViews group objects are sent to an R data.frame that contains both column and row identifiers. Series objects may be grouped into a data.frame by including the “rtype=data.frame” and “name=” options. Certain series objects may be written as R time-series objects using the “rtype=ts” option. Objects may be grouped into an R list object by including the “rtype=list” option. Series and group objects are always filtered by the current sample unless you specify an explicit sample in the xput command. Example The following commands show you how to move data from EViews into R, how to use EViews to run an R command, and how to retrieve results from R into EViews. 'create a workfile wfcreate u 100 'create some data series y=rnd series x1=rnd series x2=rnd 'open a connection to R with upper-case default output names xopen(type=r, case=upper) 'put regressors and dependent variable into R xput(rtype=data.frame, name=vars) y x1 x2 'run a command to perform GLM xrun "z<-glm(Y~X1+X2, family=Gamma(link=log), data=vars)"
- 190. EViews COM Automation Client Support (MATLAB and R)—183 xrun "summary(z)" 'retrieve coefs xget(name=beta, type=vector) z$coef 'create EViews equation equation e1.glm(family=gamma,link=log) y c x1 x2 show e1 show beta 'close R connection xclose The program first creates a workfile, then creates some series objects. The series Y is the dependent variable, and the series X1 and X2 and a constant are regressors. xopen is used to open a connection to R, and then xput is used to pass the series into an R data.frame con- tainer, which we name “VARS”. Note that the names of the data.frame, and its contents are all uppercased in R since the connection was opened with the “case=upper” option. xrun is used to estimate a GLM equation in R, with the results being stored in a GLM output object, “z”. xget is used to retrieve the coefficient member of z back into EViews, where it is stored as a vector called BETA. Finally, the same GLM specification is estimated inside EViews, and the coefficient estimates are shown on the screen for comparison. Output Display Note that the statconnDCOM package does not always automatically capture all of your R output. Consequently, you may find that using xrun to issue a command that displays out- put in R may return only a subset of the usual output to your log window. In the most extreme case, you may see a simple “OK” message displayed in your log window. To instruct statconnDCOM to show all of the output, you should use enclose your command inside an explicit print statement in R. Thus, to display the contents of a matrix X, you must issue the command xrun print(X) instead of the more natural xrun X R Related Links The Official R Project website: http://www.r-project.org/ StatConn Project website: http://rcom.univie.ac.at/
- 191. 184—Chapter 7. External Connectivity EViews Database Extension Interface EViews offers built-in support for a handful of foreign database formats (e.g. DataStream, EcoWin, Haver, FRED, etc.), providing users direct access to data in these formats via the standard EViews database interface (“Foreign Format Databases” on page 335 of User’s Guide I). If data reside in unsupported database formats, users can resort to ODBC (if an ODBC driver was available) or using an intermediate file format (such as XLS, CSV or HTML) or the Win- dows clipboard to exchange data. These approaches are less convenient than working with the standard interface, and there are a number of limitations, including the inability to obtain additional attributes such as source, units, etc. alongside observation values and the fact that data brought into EViews using these approaches cannot be "linked” back to the source to allow for automatic refreshes when a workfile is loaded. To overcome these limitations, EViews Enterprise Edition supports the EViews Database Extension (EDX) Interface which allows any external data source that implements this inter- face to be opened from within EViews and used just like an EViews database. Programmers who implement a database extension for an external database format can extend EViews so that: • an EViews user can ask what objects the external database contains • an EViews user can read data objects from the external database • an EViews user can write data objects to the external database • an EViews user can browse through the contents of the external database using a cus- tom graphical interface developed specifically for the data source
- 192. EViews Database Extension Interface—185 Using EDX, a developer can offer EViews access to a external database that is indistinguish- able from built-in access to data sources. Notably, EViews built-in support for connecting to the U.S. Energy Information Administration (EIA) on-line databases was developed using an EDX interface to the EIA API. More precisely, EDX is a set of COM interfaces. Supporting a new format involves creating a small library (usually housed in a DLL) that contains COM objects that implement these interfaces. The library can be developed in many different programming environments including native C++ and Microsoft .NET. These COM objects are used by EViews to inter- act with the underlying database.
- 193. 186—Chapter 7. External Connectivity For details and extensive examples of the EDX interface, please see the whitepaper EViews Database Extension Interface Release x.x (available from our website www.eviews.com/ download/download.html).
- 194. Chapter 8. Add-ins In Chapter 6. “EViews Programming,” beginning on page 117, we explained how you can put commands in program files to repeat tasks, produce a record of your research project, or augment the built-in features of EViews. This chapter describes Add-ins, which extend the utility of the programming tools by provid- ing seamless access to programs from the standard EViews menus and command line. Creat- ing an Add-in is a simple procedure and involves little more than defining a command and menu items for your existing EViews program. Keep in mind that Add-ins aren’t just for EViews programmers. Even if you have never writ- ten an EViews program, you may take advantage of these tools by installing prepackaged Add-ins from the IHS EViews website or from third-parties. Once installed, Add-ins can pro- vide you with user-defined features that are virtually indistinguishable from built-in EViews features. What is an Add-in? Fundamentally, an Add-in is simply an EViews program that is integrated into the EViews menus and command line, allowing you to execute the program using the menus or user- defined command. In this regard, any EViews program can be used as the basis of an Add- in. More specifically, the Add-ins infrastructure lets you: • add entries to EViews global or object-specific menus to offer point-and-click execu- tion of the Add-in program. • specify a user-defined single-word global or object-specific command which may be used to run the Add-in program. • display Add-in output in standard EViews object windows. For example, suppose you have created a program to implement an econometric procedure that prompts the user for needed input. You may turn this program into an EViews Add-in that may be run by selecting a menu item or typing a command. Lastly, the Add-in might display the output in the window of an existing EViews object. Getting Started with Add-ins The easiest way to get started with Add-ins is to download and install one of the previously written Add-ins packages from the EViews website. Simply go to the main menu in EViews and select Add-ins/Download Add-ins...
- 195. 188—Chapter 8. Add-ins EViews will open the corresponding Add-ins dialog opened to the Available tab showing the list of Add-ins that are available for download from the EViews.com website. The list shows the name of the Add-in, the publication date, version, and status. The status field indicates whether the entry has not been installed (blank), has previously been installed, or has previously been installed and is out-of-date.
- 196. Getting Started with Add-ins—189 Select the desired entry to display additional information in the bottom of the dialog and to enable the Install button. Clicking on Install instructs EViews to download the Add-in and begin the installation procedure. (Alternately, you may click on the Website button and fol- low the navigation links to the Add-ins page. Download the appropriate file to your com- puter then open it using EViews by double-clicking on the file or dropping it onto the EViews application window or the application icon.) The first step in the installation procedure is to unpack the Add-in files onto your computer. By default, EViews will put the files in a sub- folder of your default directory (see “Manag- ing Add-ins” on page 198), but you may choose an alternate location if desired (you may use the “...” button on the right-hand side to navigate to a specific directory). Click on OK to proceed. Note that if you manually downloaded the Add-in from the EViews website, you need not use EViews to unpack the files. You may instead change the download file extension from
- 197. 190—Chapter 8. Add-ins “AIPZ” to “ZIP” and use your favorite ZIP file tools to extract the contents into the desired directory. Next, EViews will prompt you to run the installation program that is included in the Add-in package. The installation program is a file con- taining EViews commands that auto- matically registers the Add-in by defining menu entries and commands for the program. If you click on No in response to the prompt, EViews will finish the auto- matic installation procedure without running the installation program and registering the Add-in. The Add-in program files will be on your computer but will not be integrated into the standard command or menu interface. You may, at a later time, examine and run the installation program as you would any other EViews program, or you may manually register your programs as described in “Registering an Add-in” on page 202. Click on Yes to display other add-in options and to finish registering the Add-in. If there are conflicts with existing directory names or existing Add-ins, EViews will warn you before pro- ceeding. After completion of the automatic installation procedure, EViews will report that the Add-in was installed successfully: We note that installing and running an EViews program file provided by an unknown indi- vidual has risks. Accordingly, we recommend that care be taken when installing packages from non-trusted sites. All of the packages provided on the EViews website have been exam- ined to ensure that they do not include potentially harmful commands. Once your Add-in is installed, you may wish to consult the Add-in documentation to obtain additional information on features, options, and commands. Open the Add-ins management dialog by selecting Add-ins/Manage Add-ins... from the main EViews menu, select the Add- in of interest and click on the Docs button to display any documentation provided by the author:
- 198. Using Add-ins—191 Using Add-ins Add-ins are integrated into the EViews menus and command line so that they work much like built-in routines. To run an Add-in program, simply select the corresponding menu entry or issue the appropriate command. Beyond that, working with Add-ins menu and command entries does require some under- standing of the difference between the two types of Add-ins: object-specific and global. As the names suggest, object-specific Add-ins are designed to work with a single object type, while global Add-ins are designed to work more generally with more than one object or object type. For example, an Add-in that computes a spline using data in a series is likely to be object- specific, since it operates on a single series, while an Add-in that copies tables, graphs, and spools into an RTF file would naturally be defined as global. The menu entries and form of commands differs between the two Add-in types. • Global Add-ins have menu entries that appear only in the main Add-ins menu. Global add-in commands follow the EViews command syntax: command(options) [args]
- 199. 192—Chapter 8. Add-ins • Object-specific Add-ins have menu entries that appear in both the main Add-ins menu and in the menu of objects of the specified object type. Object-specific Add-in com- mands follow the standard EViews object command syntax: object_name.command(options) [args] Suppose, for example, we have a global Generate time-series data Add-in with associated command tsdgp. Since the Add-in is global, it will have a menu item in the main Add-ins menu, Moreover, the global command tsdgp(diff="2", seed=100, meanconst="2", ar="0.1", ma="0.15", varconst="0.8", arch = "0.15", garch="0.2 0.2") y will run the Add-in program with the specified options. Suppose, in addition, that we have two equation-specific Add-ins, Simple rolling regression and Advanced rolling regression, with associated object-specific commands, roll and advroll. If equation EQ1 is the active object, the main Add-ins menu will contain both the global (Generate time series data) and the two equation-specific entries (Simple rolling regression and Advanced rolling regression):
- 200. Using Add-ins—193 In contrast, the EQ1 equation object will have an object Add-ins menu contains only the two object-specific entries: To run the simple rolling regression Add-in you may select either the main or the equation menu Add-ins entries, or you may enter the equation object command: eq1.roll
- 201. 194—Chapter 8. Add-ins in the EViews command window. If you wish to see the available Add-ins and their types, you may click on the Add-ins/Man- age Add-ins... entry in the main menu to display the Add-ins management dialog. EViews will display the list of installed Add-ins with a Type column showing the type associated with each entry: Note that you may use the Add-in type drop-down to filter the display. In this example, the Recshade (Add USA Recession Shading, ROLL (Simple Rolling Regres- sion), and Advroll (Advanced Rolling Regression), Add-ins are all object-specific, while the Tsdgp (Generate time series data) Add-in is global. Add-ins Examples To further illustrate the use of Add-ins, we examine two of the Add-ins currently available for download from the EViews website. (To follow along with these examples, we recommend that you first download and install the corresponding Add-in using the steps outlined in “Get- ting Started with Add-ins” on page 187.) Summarize Equation Results Our first example is a global Add-in (EqTabs) that creates a table summarizing the results from multiple equations. If you have not already downloaded and installed this Add-in, we
- 202. Add-ins Examples—195 recommend that you do so now. We employ the workfile “Demo.WF1” (which may be found in the examples subdirectory of your EViews installation directory. To run the Add-in, go to the main EViews menu, and select Add-ins/Equation Output Table (Summary form). EViews will display a dialog prompting you for the names of the equa- tions you wish to summarize: The default entry of “e*” is sufficient for this example since we want to summarize results for the previously estimated equations EQ01 and EQ02. Click on OK to continue. EViews will display a series of dialogs prompting you to specify the headers you wish to include in the summary table, the information you wish to display, and the display format. For example, the last dialog lets you choose the standard errors and t-statistics display for- mats, along with the number of significant digits for coefficients and other statistics: EViews will use the specified settings in constructing a table that summarizes the results from all of the specified equations.
- 203. 196—Chapter 8. Add-ins You may also launch the Add-in from the command line or via batch execution of a program file. Simply enter the user-defined command: eqsumtab eq* in the command line or include it in your batch program. The command instructs EViews to run the program, displaying the set of dialogs prompting you for additional input, and con- structing the table accordingly. U.S. Recession Graph Shading Our second example uses the graph-specific Add-in (RecShade) to add shading for periods of U. S. recession (as determined by the National Bureau of Economic Research). We start by opening the graph object GT in the “Macromod.WF1” workfile (which may be found in the example files subdirectory of your EViews installation directory).
- 204. Add-ins Examples—197 Next, click on the Proc/Add-ins menu item in the graph toolbar to display the Add-ins we have installed that work with graph objects. In this case, there is only a single menu item Add US Recession Shading:
- 205. 198—Chapter 8. Add-ins You may also access the active object menu items by clicking on the Add-ins entry in the main EViews menu. When the graph object GT is the active object, the main Add-ins menu shows both the global and graph specific menu items: Selecting the Add US Recession Shading entry in either the main or graph object Add-ins menu runs the Add-in program which adds shading to the existing graph: For those who prefer to use a command, you may go to the command line and enter the user-defined object command gt.recshade to apply the recession shading. This command may also be included in a program file for batch execution. Managing Add-ins EViews offers a complete system for managing your Add-ins. To bring up the management dialog, you should select Add-ins/Manage Add-ins... from the main EViews menu:
- 206. Managing Add-ins—199 The management dialog is divided into two sections: • The Registered Add-ins section is where you will perform most of the tasks of man- aging your Add-ins (add, delete, edit, update, and reorder Add-in definitions, and view the documentation file). • The Default Add-ins directory section allows you to set the default directory for your Add-ins. Registered Add-ins The top portion of the dialog shows settings for currently installed Add-ins, with each line devoted to an Add-in.
- 207. 200—Chapter 8. Add-ins By default, all of the installed Add-ins will be displayed. You may use the Add-in-type drop- down menu to filter the list, showing for example only global or only equation-specific Add- ins. The File name column shows the name (and possibly location) of the Add-in program, and the Type column indicates whether the Add-in is global or object-specific. The Proc column shows the command keyword associated with the Add-in (if any), while the Menu Text col- umn shows the text used in the user-defined menu entries. The Version column show the version number of the Add-in. You may use the buttons and arrows on the right-hand side of the dialog to manage your Add-ins: • To add a new Add-in to the list, simply click on the Add button to display the Add/ Edit Program dialog. The Add/Edit Program dialog is described in detail in “Regis- tering an Add-in” on page 202. • To delete an Add-in, simply click on the name in the Add-ins management dialog and press the Remove button. • The order in which your Add-ins appear in the menus may be controlled using the up and down arrows. If you have many Add-ins, putting the most frequently used Add-ins first in the list may simplify menu access (see “Menu Congestion” on page 205). In addition, the order in which Add-ins appear can have important conse- quences in the event that Add-ins duplicate command names (see “Command Mask- ing” on page 204). • To edit the settings of an existing Add-in, select it from the list and chick on the Edit button to display the Add/Edit Program dialog. The Add/Edit Program dialog is described in detail in “Registering an Add-in” on page 202.
- 208. Creating an Add-in—201 • To examine the documentation file associated with an Add-in, click on the name and press the Docs button. • To check whether the Add-in has an updated version available, and to install the update if available, click on the Update button. Note you may select multiple Add-ins at the same time and click on the Remove or Update buttons to perform the specified operation. You may also right click anywhere on the list of Add-ins and select Update All to check for updates on all of your Add-ins. After modifying the desired settings, click on OK to accept any changes. Default Add-ins Directory The bottom portion of the Add-ins dialog shows the current default Add-ins directory. The default directory is where an Add-in will search for supplementary files if explicit directory locations are not provided. To change the default directory, click on the button on the right and navigate to the desired directory, or simply enter the desired folder name. Click on OK to accept the settings. Creating an Add-in You can use Add-ins developed by others without ever having to create one yourself. Indeed, many users will never need to go beyond running Add-ins downloaded from the EViews website or other repositories. You may find, however, that creating your own Add-ins is both useful, if only to add a menu item or assign a one-word command for running your favorite EViews program, and easy-to- do. Assuming that you already have an EViews program, creating an Add-in requires at most two steps: • Register the program as an Add-in. Registering an Add-in is the process of defining the Add-in type and assigning menu entry and command keywords to an EViews pro- gram. • (optional) Create an Add-in package for distribution. Bundling your program files into a self-installing package file and providing a program for automatically registering the Add-in means that others can more easily incorporate your Add-in into their EViews environment. While only the first step is required, you should consider the optional step of creating a self- installing Add-in package if you wish to distribute your Add-ins more widely.
- 209. 202—Chapter 8. Add-ins In the remainder of this section we describe the steps required to register an Add-in. In addi- tion, we provide design tips and describe additional programming tools that will aid you in writing sophisticated Add-in programs. Registering an Add-in The process of defining the Add-in type and assigning menu entry and command keywords to an EViews program is termed registration. The Add-in registration process may also be used to associate documentation with the Add-in. To register an EViews program file as an new Add-in, click on the Add-ins/Manage Add- ins... menu entry in the main EViews menu. The top portion of the management dialog shows the list of currently registered Add-ins: Click on the Add button to display a standard file Open dialog. Navigate to the program file you wish to register and click on OK to continue. EViews will display the Add/Edit Program dialog with various default settings.
- 210. Creating an Add-in—203 The dialog allows you to specify a command keyword for the Add-in, to add menu items for point-and-click access to the Add-in, and to attach documentation and descriptions. • The Program file edit field in the dialog should be used to specify the program to be used as an Add-in. You may enter the name of a file (with an absolute or Add-ins directory relative path, if necessary) or you may click on the button to the right-hand side of the edit field to navigate to the file. • The Documentation file edit field allows you specify a PDF, Text, RTF, Microsoft Word file, or URL containing documentation for the Add-in. Note that relative paths are evaluated with respect to the directory of the Add-in program, not the default Add-in directory. • The Menu/Procedure type dropdown setting determines whether the program is a global or an object specific Add-in. (Recall that global Add-ins are those designed to work with multiple objects or object types, while object-specific Add-ins work with a single object and object type.) • If you check Assign as Command or Proc, EViews will add the specified single-word command or proc keyword to the EViews language, so you may run the Add-in using a global or object command. Names for the keyword must follow standard EViews command naming convention. Global Add-ins should not be given the same name as built-in EViews commands. (See “Command Masking” below, for additional dis- cussion.) • If you check Include in Add-ins menu, EViews will add the specified Menu text to the appropriate menus.
- 211. 204—Chapter 8. Add-ins • You may use the Brief description edit field to describe the purpose of the Add-in. • You may enter a Version number for your Add-in and an Update URL indicating where to find the update XML file (see “XML File Specification” on page 207). In the example above, we instruct EViews to use the file “Rtf_out.PRG”, which is located in the default Add-ins directory, as an Add-in program. We indicate that the program is a global Add-in that may be run from the main EViews menu by selecting the Add-ins menu item Output to RTF or by issuing the command rtf_out. There is no documentation file. Add-ins Design Issues For the most part, defining commands and menu items for an Add-ins is straightforward. There are, however, a few issues that you should bear in mind when designing your Add-in. Command Masking Allowing you to define user-specified Add-in command names opens up the possibility that an Add-in will be assigned the same command as an existing EViews command, or that mul- tiple Add-in programs will be assigned the same name. Duplicate command names will gen- erate an error or will lead to command masking where some of the instances will be ignored. • If you specify a global Add-in command name that is identical to an EViews command or a previously defined global Add-in command, EViews will issue an error message, and will display the Add/Edit Program dialog so you can provide a different name. EViews will not, for example, permit you to register an Add-in with the global com- mand copy since this conflicts with the built-in command. • EViews will not generate an error message if you provide an object-specific command name that is identical to a built-in command, but the EViews command will mask the user-defined command. EViews will, for example, permit you to register an equation command with the name resid, but if you enter the equation command “eq01.resid”, EViews will display the built-in resid view of equation EQ01, instead of running the user-defined Add-in. • If you specify an object-specific Add-in command name that is identical to another object-specific command, EViews will not error. When multiple Add-ins are assigned the same object-specific command, the first Add-in listed in the list of registered Add- ins will be used and the remainder will be masked. To eliminate masking, you must edit the conflicting Add-in command definitions. If you wish to retain the same com- mand name for multiple Add-ins, you should use the Add-ins management dialog to reorder the Add-ins list so that the desired Add-in has priority (“Managing Add-ins” on page 198).
- 212. Creating an Add-in—205 We emphasize that masking only occurs within like Add-ins. You may have a global, series, and group Add-in that each use the same command without experience masking, but two global Add-ins or two series Add-ins with the same name will lead to masking. Menu Congestion While you may define as many Add-in menu items as you like, EViews does place limits on the number of menu items that may be displayed by default. If you have more than 10 global menu entries or more than 10 object-specific menus of a given type, the corresponding menus will display the first 10 entries, along with an 11th menu entry, More... Clicking on More... displays a listbox showing the full set of entries. In such circumstances, we recom- mend that you reorder your Add-ins so that the most used Add-ins are given priority. Multiple Object Add-ins You may wish to use a single program file to define Add-ins for several object types. Since it is not possible to use a single Add-in entry to define multiple object-specific types, you must create separate entries for each object-type which point to the single program file. For each relevant object type, select Add-ins/Manage Add-ins... from the main EViews menu, then navigate to and select the program file. Use the Add/Edit Program dialog to define the object-specific entry. Creating an Add-in Package If you are developing Add-ins for use by others, we recommend that you create a self-install- ing package so that users may easily incorporate your Add-ins in their EViews environment. You can then host your package on a company intranet or perhaps submit your package to be hosted on the EViews.com website for wide distribution. The process of creating an Add-in package is straightforward, requiring at most two steps: • (optional) Create a table of contents (TOC) information file and installer program file. • Create a self-extracting Add-in package file containing the program and support files (including the TOC and installer program file, if available). The second step, creating the self-extracting package, is trivial. Simply create a standard ZIP archive file containing all of the files for your Add-in, then rename the ZIP file so that it has the extension “AIPZ”. You now have an self-extracting Add-in package file. Opening a self-extracting package file, either automatically by a browser after completing the download, by double clicking on the file, or by dropping it onto EViews, will prompt you to unpack and copy the files to the location of your choice. The Add-in will not, however, automatically be installed and registered unless you include a table of contents (TOC) information file and installer program along with your program files. Creating the TOC and installer program files takes only a few minutes and allows you to
- 213. 206—Chapter 8. Add-ins automate the Add-in installation and allow for automatic updating of your Add-in as you provide newer versions. We strongly recommend that package distributors take the time to create these files as described below. Table of Contents First, you should create a table-of-contents file named “Toc.INI” for inclusion in your pack- age. The TOC file should contain setup information which describes the directory in which the Add-in files should be installed, and if appropriate, the name of an EViews installer pro- gram for registering the Add-in. The format of the TOC file is: [package] installer = <name of installer file> folder = <name of folder to create> A TOC file should always begin with the line “[package]”, followed by lines which give the directory and installer information. The installer keyword is used to indicate the name of the EViews program file, if one exists, that should be run to register the Add-in (see “Installer Program” on page 207). If, for example, a registration program file named “Recession shade install.PRG” is included in your package, you should include the line installer = recession shade install.prg If you include this line in your TOC, EViews will automatically run the installer program when it opens the AIPZ package. If you do not wish to provide an installer program, you should include the line “installer=none” in the TOC file. The folder keyword may be used to indicate a subfolder of the default Add-ins directory into which you will extract the packaged files. Thus, folder = RecShade tells EViews to extract the contents of the AIPZ file into the “RecShade” folder of the Add-ins directory. If no folder is specified, the name of the AIPZ file will be used as the target folder name. Additionally, you may use the special folder name “<addins>” to indicate that the contents of the AIPZ archive should be placed in the main Add-ins directory. (Note, how- ever, that only folders in an AIPZ archive may be written to the main Add-ins directory in this fashion; individual files in AIPZ files must be written into subdirectories. We emphasize that creating a TOC file and providing an installer program are not required. In the absence of a TOC file or an installer= specification, EViews will, after unpacking the AIPZ files, simply display a message reminding the user that the installed programs may be registered manually using the Add-ins management dialog.
- 214. Creating an Add-in—207 Nevertheless, we strongly recommend that package distributors provide both a TOC file and installation program to facilitate use of their Add-ins. Packages hosted on the EViews web- site must include both a TOC and an installer. Installer Program Next, you should create a simple EViews program that uses the addin (p. 288) command to register the Add-in with appropriate type, menu, and command settings. Note that the TOC file installer= specification should point to this installer program. For example, the graph-specific Add-in described in “U.S. Recession Graph Shading” on page 196 may be registered by including the following command in a program file: addin(type="graph", menu="Add USA Recession Shading", proc="recshade", docs=".recession shade.txt", desc="Applies US recession shading to a graph object.") ./recshade.prg The options in this example should be self-explanatory. The command registers the program “./recshade.PRG” as a graph-specific Add-in with menu item “Add USA Recession Shading”, command name “recshade”, and description text “Applied US Recession shading to a graph object”. See addin (p. 288) for details and a complete list of options. Use of the following addin options is highly recommended: Documentation We recommend that you provide documentation for your Add-in, and use the “docs=” option to point to the documentation file. Documentation could be anything from a simple text file with some syntax hints, to a lengthy PDF document that describes the Add-in features in detail. Version EViews allows Add-ins to have a version number which allows the users of your Add-in to use automatic updating to ensure they have the latest version of the Add-in. When a user uses the Update button on the Manage Add-ins dialog to check for Add-in updates, EViews will compare the hosted version number with the currently registered version number and download the latest version if necessary. You may use the “version=” option to specify the version number. If omitted, EViews will assume that the Add-in version number is 1.0. XML File Specification One of the most useful Add-ins management features is the ability of users to automatically update their installed Add-ins as newer versions become available. To support this feature,
- 215. 208—Chapter 8. Add-ins EViews must know where to look to determine the most recent version of the Add-in and where to download any updates. This information is communicated in an XML file, typically located on the Add-ins package hosting site. If you will be hosting this file, you should use the addin option “url=” to spec- ify the URL for the XML file. If this option is not supplied, EViews will look for the XML file on EViews.com. The XML file should contain one or more item definitions, where each item contains infor- mation on a specific Add-in. An item definition is contained in the lines between an <item> and </item> tag. An example of the full specification of an item is as follows: <item> <title>BMA</title> <path>bmabma.prg</path> <path>bmabmamlogit.prg</path> <version>1.0</version> <description>Computes different Bayesian Model Averaging methods including LM, GLM and Multinomial Logit models.</description> <link>http://eviews.com/Addins/BMA.aipz</link> <pubDate>13 Mar 2012</pubDate> </item> Note that the only required specifications are the <title> and <link>. The <path> specification is used to identify the paths and file names of the main program files used by the Add-in, with the path location specified relative to the Add-ins directory. Automatic updating will update these files when a newer Add-in version is available. If <path> is not specified, EViews will use the <title> specification to determine the rele- vant Add-in proc name, and use registration information for the proc name to determine the files to update. When an add-in package has multiple main program files, a <path> statement is required. You should list each file using a separate <path> entry. In the example above, the BMA Add- in has two program files, called “bma.PRG”, and “bmalogit.PRG” that are associated with procs. EViews will update all of the files associated with these procs when updating the Add-in. The <version> is used to specify the current Add-in version number. When the user checks for updates, EViews will download the updated version if the version number they have cur- rently installed is lower than the one given in the <version> tag. Finally, the <link> specification contains the URL (or network file location) of the AIPZ file containing the Add-in. This is the location from which EViews will download the updated Add-in package should the user request an update.
- 216. Add-ins Design Support—209 The <description> and <pubDate> specifications should be self-explanatory. Add-ins Design Support EViews offers several programming language features that will aid you in developing and working with Add-ins. Add-ins Registration Command The addin command may be used to register an EViews program file as an Add-in. You may use the command to specify the Add-in file, Add-in type, menu text, user-defined command name, description, version number, documentation file, XML file, etc. See addin (p. 288) for details. The Active Object Keyword “_this” Keyword Central to the construction of an object-specific Add-in program is the ability to reference the object on which the program should act. If, for example, you wish to write an Add-in that computes a statistic based on the data in a series object, you must have a convenient method of referring to the series. Accordingly, EViews provides an object placeholder keyword, _this, which refers to the currently active object upon which a program may operate. Typically the use of _this in an EViews program indicates that it has been designed to work with a single object. There are three ways in which the identity of the _this object may be set: • _this refers to the active object whose window was last active in the workfile; when used in a program, _this refers to the active object at the time the program was run. • executing an Add-in using the object-command syntax, obj_name.proc, sets _this to obj_name. • _this can be set to a specific object using the “this=” option in an exec or run com- mand. While the above description is a bit abstract, a simple example should illustrate the concepts that lay behind the three methods. Suppose we have the trivial (silly) program “Myline.PRG” which consists of the command: _this.line First, if we register this program as a global Add-in with menu item text “Show line”, we can display a line graph of a series or group object by opening the series or group and selecting Show line from the Add-in menu. From the program’s point of view, the _this object is
- 217. 210—Chapter 8. Add-ins simply the opened series or group whose menu we are using (the last one active in the workfile). Alternately, if we had registered the program as a series-specific Add-in with proc name “myl”, the command: ser01.myl identifies SER01 as the _this object, so that the object used by the Add-in will be the series SER01, regardless of which object is active in the workfile. Lastly, you may specify _this explicitly when using the exec or run command to run the program by including the “this=” option to identify an object by name. The command: exec(this=ser01) myline.prg explicitly specifies SER01 as the _this object. Custom Object Output EViews allows you to display the contents of a table, graph, or spool object in the window of another object. This feature will undoubtedly most often be used to mimic the behavior of EViews views and procedures in Add-ins programs. Suppose, for example, that your Add-in program performs some calculations and constructs an output table TABLE_OUT. You may instruct EViews to display the contents of TABLE_OUT in the object OBJECT_01 using the display command: object_01.display table_out Thus, a useful approach to constructing an object-specific Add-in involves creating a pro- gram of the form: [use _this to perform various calculations] [create an output table or graph, say the table TABLE01] ’ put the output in the _this window _this.display table01 delete table01 Note that we delete the TABLE01 after we put it in the window of _this. (You may instead wish to employ local subroutine to enable automatic cleanup of the temporary table.) If the above program is registered as a series-specific Add-in with the command “FOO”, then you may run it by issuing the command series01.foo which will display the output in the SERIES01 window.
- 218. Add-ins Design Support—211 The display object command is a documented view for each supported object. See for example, Series::display (p. 554) in Object Reference.
- 220. Chapter 9. User Objects As the name suggests, the EViews user object allows you to create your own object types inside of EViews. A user object may be as simple as a storage container for other EViews objects, or it may be a sophisticated new estimation object defined by multiple EViews pro- grams, with views containing post-estimation tests and results, and procedures producing output from the estimation results. Once defined, a user object is almost indistinguishable from a built-in EViews object. Defining a user object is quite easy—simply specify the types of data and objects stored inside your object, and if desired, define a set of views and procedures that be accessed via commands, menus and dialogs. Even if you do not go to the trouble of creating your own objects, you may take advantage of this powerful tool by using user objects downloaded from the IHS EViews website or obtained from third-parties. What is a User Object? An EViews user object is a custom object that can contain data and objects and may offer views and procedures. In its simplest form, a user object is a storage container for EViews objects. More sophisticated user objects also provide views and procs that allow you to run EViews programs to perform various tasks and display results. These latter objects work almost identically to built-in EViews objects such as a series or equation. In the discussion to follow it will be important to distinguish between user objects that are unregistered or registered: • Unregistered user objects are simple container objects which require virtually no effort to create. • Registered user objects are more powerful than unregistered user objects. Registering a user object class is the process of describing what happens each time a new instance of the user object is created, and defining data and a set of views and procs available to the user object class. A relatively complex registered user object might be a complete econometric estimator. Each time a new instance of the estimator object is created, it could specify and perform estima- tion, saving results inside the user object in the form of data objects such as coefficient vec- tors and covariance matrices. The object could also offer views such as coefficient tests and procs to perform forecasting. And like an EViews equation object, you may have multiple instances of this estimator in the workfile, each corresponding to a different set of estimates.
- 221. 214—Chapter 9. User Objects We note that a registered user object need not be particularly complex. For example, you could have a simple user object called “RESULTS” that contains a collection of graphs, tables, and estimation objects obtained from a particular form of analysis. You could define simple views for your user object that display the stored tables or graphs, and define procs that let you extract those tables or graphs into new EViews objects. Registering the object allows you to have multiple results objects in your workfile. Unregistered User Objects To create a new, unregistered user object, select Object/New Object in the main EViews menu. Scroll down and select UserObj, enter a name for the object in the workfile (in this example, MYOBJ), and click on OK. Alternately, you may enter the generic user object declaration command userobj myobj to create a user object named MYOBJ in the workfile.
- 222. Unregistered User Objects—215 Notice that MYOBJ has a black icon. A user object created in this fashion is empty, with no user-defined views or procedures. Double-clicking on MYOBJ opens the object and displays its contents: As you can see, the object is empty. Clicking on the View menu of the object shows only a single Label entry. The Proc menu is completely empty. An empty, unregistered userobj is not particularly interesting. You may, however, use the object as a container for EViews matrix objects (including scalars), string objects, estimation objects, and view objects (graphs, tables, spools): • The add (p. 811) and drop (p. 814) procs may be used to populate the user object and the extract (p. 814) proc may be employed to extract objects into the workfile. • You may use user object data members to provide information about the contents of the object: the @hasmember(obname) member function can be used to determine whether obname exists inside the user object, while @members returns a space delim- ited string listing all objects in the user object.
- 223. 216—Chapter 9. User Objects See “User Object Programming Support” on page 236 for details. The following program offers a simple example showing the use of these commands: userobj myobj myobj.add mygraph myobj.add mytable %list = myobj.@members myobj.drop mygraph myobj.extract mytable mynewtable The first line creates a new, empty, user object called “MYOBJ”. The second and third lines copy the workfile objects “MYGRAPH” and “MYTABLE” into MYOBJ. The fourth line creates a string variable whose contents are “mygraph mytable”. The fifth line removes MYGRAPH from MYOBJ, and the final line copies MYTABLE back into the workfile under the name MYNEWTABLE. Registered User Objects While simple, unregistered user objects may only be employed as storage containers, regis- tering a user object class creates a more powerful working environment. Note that we used the term user object class, reflecting the fact that when you register a user object, you are not simply declaring a single object but rather are defining the general characteristics of a type of object. Registering a user object class allows you to have multiple objects of a given type in your workfile, each of which has its own data. Additionally, registering allows you, if desired, to define views and procs that can be used by any object of that class. These views and procs will execute a set of EViews programs that you specify as part of the registration procedure. While not difficult, creating a registered user object class is a bit more involved than creating an unregistered user object. Details are provided in “Defining a Registered User Object Class” on page 230. You may, of course, download and register user object classes created by others. Since work- ing with example user objects provides good introduction to this powerful tool, we begin by discussing the steps required to download an object from the EViews website. Downloading a Registered User Object To download and install object class definitions from the EViews website, simply select Add- ins/Download User Objects...from the main EViews menu.
- 224. Registered User Objects—217 EViews opens the User objects management dialog opened to the Available tab, which shows a list of the user objects classes (in this case ResStore, Roll, and BiProbit), that are available for download along with the date they were published, their version number, and their status (blank for un-installed, installed, or installed but out of date):
- 225. 218—Chapter 9. User Objects Selecting an entry displays a description of what the user object does below the listbox. Clicking on the Install button downloads the selected user object and prompts you to install the package on your the local computer. (Alternately, you may click on the Website button and follow the navigation links to the user objects page. Download the appropriate file to your computer then open it using EViews by double-clicking on the file or dropping it onto the EViews application window or the application icon.) The first step in installa- tion is to unpack and copy the files to your computer. By default, EViews will put the files in a sub-folder of your default directory (see “Default User Objects Directory” on page 230) but you may choose an alternate location if desired (you may use the “...” button on the right-hand side to navigate to a specific directory). Click on OK to proceed. To unpack the files without using EViews, simply change the download file extension from “AIPZ” to “ZIP” and use your favorite ZIP file tools to extract the contents into the desired directory. Next, EViews will prompt you to run and installation program that is included in the user object package. If you click on No in response to the installation prompt, EViews will finish the automatic install procedure without running the installation program and registering the userobj. You may later examine the installation program prior to running it as you would any other EViews program, or you may manually register your object as described in “Registering a User Object Class” on page 232. Click on Yes to finish the installation and registration. If there are conflicts with existing directory names or existing user objects, EViews will warn you before proceeding. After completion of the automatic installation procedure, EViews will report the status of the installation:
- 226. Examples—219 We note that installing and running an EViews program file provided by an unknown indi- vidual has risks. Accordingly, we recommend that care be taken when installing packages from non-trusted sites. All of the packages provided on the EViews website have been examined to ensure that they do not include potentially harmful commands. Working with Registered User Objects Once you have registered your user object you may work with it much as you would any built-in EViews object. You can create a new instance of the object using the Object/New Object... main menu item, or by declaring it on the command line using the name of the object and any relevant options or arguments: userobj_class_name(options) my_objname [args] You may use the defined views and procs of the object using the object View or Proc menu, or via the command line using the standard syntax: userobj_name.view_name(options) [args] userobj_name.proc_name(options) [args] The user object data member @-functions may be accessed using the syntax: [result_type] result = userobj_name.@datamember_name[(arg)] Examples To illustrate the use of registered user objects, we examine two of the EViews user objects that are currently available for download from our website. (To follow along with these examples, we recommend that you first download and install the corresponding user object using the steps outlined in “Downloading a Registered User Object” on page 216.) The first example uses a simple container user object (of type ResStore) that allows you to add, extract, and display graphs, tables and estimation objects. The second example per- forms rolling regression estimation using the Roll user object. Simple Container Object (ResStore) This first example uses the ResStore user object to create a storage container for objects from our workfile. This is a bare bones registered object that nonetheless shows the basic features of registered user objects. We use the workfile “Demo.WF1” (which may be found in the example files subdirectory of your EViews installation directory) and assume that you have already installed the ResStore object class.
- 227. 220—Chapter 9. User Objects You may create a new ResStore object by clicking on Object/New Object... and then select- ing resstore in the list of object types. Notice that while the built-in EViews objects are listed alphabetically, the two user objects (ResStore and Roll) are simply placed at the bottom of the listbox. Select resstore and spec- ify the name STOREDOBJECT for our new object. Click on OK to create the object. Alter- nately, enter the command resstore storedobject in the EViews command line and hit ENTER. As part of its construction, the ResStore object will display a dialog asking you to enter the names of the workfile objects you would like to store: You may enter the names of any matrix objects (including scalars), strings objects, estima- tion objects, or view objects (graphs, tables, spools).
- 228. Examples—221 Note that there is a check-box that lets you specify whether to remove the objects from the workfile after storing them. You should selection this option if you wish to move, rather than copy, the specified objects into the ResStore. Once you hit OK, a new ResStore object named STOREDOBJECT will be added to your workfile. If you open up the ResStore object, a spool view display of all objects currently stored is shown: You may use the View menu to access the defined views for this type of object which allow you to show various subset types of the objects in the container: Similarly, the Proc menu lists procs which allow you to add, remove, and extract objects from the storage container:
- 229. 222—Chapter 9. User Objects As with other EViews objects, you may use the command language to work with the ResS- tore object. For example the defined view command, storedobject.graphs displays all of the graph objects in the object storedobject.extractobjects extracts all of the objects from STOREDOBJECT into the workfile. The command string storednames = storedobject.@members saves a list of the stored object names in the string object STOREDNAMES. Rolling Regression Estimation Object (Roll) Our second example uses the Roll user object to estimate rolling regressions. We again use the workfile “Demo.WF1”, and we assume that you have already installed the Roll object class. You can create a new Roll object by clicking on Object/New Object... and then selecting roll in the list of object types, or by entering the roll command followed by the name of a new object in the command line: roll myroll As part of its creation, the Roll object will display a series of dialogs prompting you to pro- vide information on how the object should be constructed. First, you will be asked whether to create the new object using the specification from an existing equation or by specifying an equation manually:
- 230. Examples—223 Since we will use one of the previously estimated equations in our workfile as the basis of our rolling regression, click on OK to accept the default. Next, you will be asked to select your base equation and to specify the basic type of rolling regressions you wish to perform: To obtain recursive estimates based on equation EQ01 choose EQ01 in the drop-down menu and select Anchored at start. Click on OK to continue. Lastly, you will be prompted to provide sample information and a step size: Click on OK to create the Roll object using the specified settings. (Note that if you had chosen Manual equation specification in the first dialog or Fixed window estimation in the second dialog, the subsequent dialogs would provide a different set of options). EViews estimates the rolling regression and, like built-in estimation objects, displays basic estimation information in the object window:
- 231. 224—Chapter 9. User Objects These basic results may be viewed at any time by selecting View/Summary in the object view menu or by entering the object command myroll.summary in the command line. Next, consider the custom views that have been defined for this object. In addition to the summary view, you may display information on the coefficient statistics, residual statistics, likelihood statistics, members of the object, and the standard label information view for the Roll object: Roll: MYROLL Roll type: Anchored at start Specification: EQ01 Estimation command: ROLL(AS,STEP=1,ANCHOR=1953Q1) MYROLL @ EQ01 Anchor point: 1953Q1 Number of subsamples: 160 Number of coefficients: 4 Step size: 1 Full sample estimation results: Dependent Variable: LOG(M1) Method: Least Squares Date: 01/31/13 Time: 10:19 Sample (adjusted): 1952Q2 1992Q4 Included observations: 163 after adjustments Variable Coefficient Std. Error t-Statistic Prob. C 1.312383 0.032199 40.75850 0.0000 LOG(GDP) 0.772035 0.006537 118.1092 0.0000 RS -0.020686 0.002516 -8.221196 0.0000 DLOG(PR) -2.572204 0.942556 -2.728967 0.0071 R-squared 0.993274 Mean dependent var 5.692279 Adjusted R-squared 0.993147 S.D. dependent var 0.670253 S.E. of regression 0.055485 Akaike info criterion -2.921176 Sum squared resid 0.489494 Schwarz criterion -2.845256 Log likelihood 242.0759 Hannan-Quinn criter. -2.890354 F-statistic 7826.904 Durbin-Watson stat 0.140967 Prob(F-statistic) 0.000000
- 232. Examples—225 Clicking on View/View rolling coefficient statistics... display a dialog prompting you to select the coefficients and statistics you wish to display. By default, the object will display the coefficient estimates for all of the coefficients in the regression: Click on OK to accept the default values and to display a graph of the results in the object window:
- 233. 226—Chapter 9. User Objects Equivalently, you could have issued the command: myroll.rollcoefs c log(gdp) rs dlog(pr) in the command line. Note that you could have specified a different statistic in the dialog or add the “stat=” option to the command to display a different coefficient statistic. For example, selecting P- values in the dialog or entering, myroll.rollcoefs(stat=pvals) c log(gdp) rs dlog(pr) displays the coefficient t-statistic p-values:
- 234. Examples—227 Similarly, you may click on the Proc menu to display a list of the user defined procs: The first two entries re-initialize the Roll object using the dialogs we first encountered when creating MYROLL. The next three menu entries extract results into the workfile. For exam- ple, clicking on Extract rolling residual statistics... opens a dialog prompting you to iden- tify the results you wish to extract along with the destination:
- 235. 228—Chapter 9. User Objects Clicking on OK t saves the statistics in the workfile in the series ROLL_R2. The com- mand myroll.extractresidstat(stat=r2s) roll_r2 performs an equivalent operation. It is worth noting that behind-the-scenes in this object is a set of user programs that use standard EViews programming tools to display dialogs, perform computations, and display and extract results. Managing User Object Classes To manage your user object definitions, select Add-ins/Manage User Objects... from the main EViews menu. R 2
- 236. Managing User Object Classes—229 EViews will display the User objects management dialog opened to the Installed tab: The top portion of the dialog shows settings for currently registered user object classes. The Name column shows the name of the class, the Definition file column displays the location and file name of the definition “.INI” file for the class (see “Creating an Object Definition
- 237. 230—Chapter 9. User Objects File” on page 231), and the Version column shows the version number of the user object class. Note that you may click on the column headers in the list to sort the list by the contents of column. You may use the buttons on the right-hand side of the dialog to manage your classes: • To add a new user object to the list, simply click on the Add button to display the Add/Edit User Object dialog. The dialog settings are described in “Registering a User Object Class” on page 232. • To delete a class, simply click on the name in the Add-ins management dialog and press the Remove button. • To edit the settings of an existing user object class, select it from the list and click on the Edit button to display the Add/Edit User Object dialog. The dialog settings are described in “Registering a User Object Class” on page 232. • To open and edit the INI definition file for the user object class, select it from the list and click on the Open button. • To examine the documentation file associated with a class, click on the name and press the Docs button. • To check whether the userobj class has an updated version available, and to install the update if available, click on the Update button. Note you may select multiple classes at the same time and click the Update button to update the set. You may also right click anywhere on the list of user object classes and select Update All to update all of your classes. Default User Objects Directory The bottom portion of the Installed tab shows the default user objects directory. The default directory is where user objects will be installed and where the user object programs will search for supplementary files if explicit directory locations are not provided. To change the default directory, click on the button on the right and navigate to the desired directory, or simply enter the desired folder name. Click on OK to accept the settings. Defining a Registered User Object Class To define a registered user object class, you must provide information on how to construct (create) an instance of the object. While the constructor information is all that is required, you may optionally specify menu items and custom command names for views and procs that will execute EViews programs. We may divide the registration procedure into two distinct steps:
- 238. Defining a Registered User Object Class—231 • Create an object definition file which includes constructor, view, and proc definitions. • Register the object definition file with EViews. This discussion assumes that you have already written programs to initialize your object and possibly to display views and execute procs. These programs will be standard EViews pro- grams that use ordinary EViews commands. There are, however, several programming fea- tures which are designed specifically for user object (and Add-in) program development that you should find useful. See “Add-ins Design Support” on page 209 in the Command and Pro- gramming Reference. Creating an Object Definition File The object definition file is a simple text file with a “INI” extension. This file describes how to construct the custom object using an EViews program and optionally provides menu items and custom command names for views and procs that will execute EViews programs. There are three sections in the file, corresponding to the constructor, the views, and the procs of the object. The first section of the definition file should start with a line consisting of the text “[con- structor]” (without the quotes). The line immediately following should contain the path and name of an EViews program file that will be used as the constructor. The constructor pro- gram file describes how the object should be initialized, or constructed, when you create a new instance using the Object/New Object... menu item or the command line. The second section contains the view definition specifications. This section should start with a line consisting of the keyword “[views]”. Each line following this keyword will define a view for the user object. Each view definition line should consist of the menu text, fol- lowed by a comma, a custom command name for the view, a comma, the path and name of the program file to be run when the view is selected from the menu or run from the com- mand line. The third section contains the proc definition specifications. It follows the same format as the views section, but begins with “[procs]” rather than “[views]”. Note that when providing the path of the programs in your definitions, the “.” shortcut can be used to denote the folder containing the definition INI file. For example, the following is the definition file for the ResStore user object: [constructor] ".resstore construct.prg" [views] "View stored objects", objects, ".viewall.prg" "View stored equations", equations, ".viewequations.prg"
- 239. 232—Chapter 9. User Objects "View stored graphs", graphs, ".viewgraphs.prg" "view stored tables", tables, ".viewtables.prg" [procs] "Extract objects from store", extractobjects, ".extract objects.prg" "Add objects to store", addobjects, ".resstore construct.prg" "Remove objects from store", dropobjects, ".remove objects.prg" The ResStore user object use a constructor program called “resstore construct.PRG” which is called when you create a new ResStore object from the dialogs or command line. There are four view menu items, each associated with a different EViews program. The first view definition tells EViews that it should create a view menu item View stored objects and object command objects, and associate both with the EViews program “viewall.PRG” (which is located in the ResStore directory). This definition means that selecting View/View stored objects from the object menu, or entering the object command my_object.objects will run the “viewall.PRG” program which displays all of the stored objects in MY_OBJECT. Similarly, there are three proc menu items. Selecting Proc/Remove objects from store or issuing the command my_object.dropobjects runs the “remove objects.PRG” program which displays a dialog prompting you for the name of the objects to remove from MY_OBJECT. Registering a User Object Class To register a new user object class, select Add-ins/Manage User Objects... from the main EViews menu to display the User objects management dialog, then click on the Add button to display the Add/Edit Program dialog.
- 240. Defining a Registered User Object Class—233 The dialog allows you to provide a name for the user object class, to specify the INI file, attach a documentation file, and provide various object attributes: • The Object name edit field should be used to specify the user object class name. Note that if you provide the name of a built-in EViews object, the EViews built-in object will take precedence. • The Definition file edit field must be used to enter the name and path of the INI definition file. Note you may press the “...” button to navigate to and select the file. • The Documentation file edit field allows you specify a PDF, Text, RTF, Microsoft Word file, or URL containing documentation for the user object class. (Note that rel- ative paths are evaluated with respect to the directory of the INI file, not the default user object directory.) • You may use the Brief description edit field to describe the purpose of the User Object. • You may enter a Version number for your user object and an Update URL indicating where to find the update XML file (see “XML File Specification” on page 235). You must provide an object name and a definition file to register your object. The remaining information is recommended, but not required. Creating a User Object Package In “Defining a Registered User Object Class” on page 230 we showed how you can use EViews program files and an object definition file to register a new user object class for per- sonal use. If you wish to distribute your custom object to others, we highly recommend that you create a user object package. Packaging the user object allows you to bundle all of the files for dis-
- 241. 234—Chapter 9. User Objects tribution and, if you provide an installer program, offers automatic installation of the object by dragging-and-dropping the package onto EViews or double clicking on the file. The process of creating a user object package is virtually identical to packaging of an EViews Add-in, with a few minor exceptions. We summarize briefly the main steps. Related discus- sion may be found in. Creating the Self-installing Package The process of creating a user object package is straightforward, requiring only two steps: • (optional) Create a table of contents (TOC) information file and installer program file to allow for automatic registration and updating of the Add-in. • Create a self-extracting user object package file containing the object definition file and any support files (including the TOC and installer program file, if available). To create the self-extractive file simply create a standard ZIP archive file containing all of the files for your user object, then rename the ZIP file so that it has the extension “UOPZ”. Opening such an file, automatically after completing the download, by double clicking on the file, or by dropping it onto EViews, will begin the automatic installation procedure. The user object will not, however be automatically installed and registered unless you include a table of contents (TOC) information file and installer program along with your pro- gram files. Creating the TOC and installer program files takes only a few minutes and allows you to support automatic registration and updating of the user object. We strongly recom- mend that package distributors take the time to create these files as described below. Table of Contents Next, you should create a table-of-contents file named “Toc.INI”. The TOC file should con- tain setup information for the user object which describes the directory in which it should be installed, and the name of the EViews program, if any, that should be run to register the user object files. The format of the TOC file is: [package] installer = <name of installer file> folder = <name of folder to create> A TOC file should always begin with the line “[package]”. The “installer” keyword is used to indicate the name of the EViews program file that should be run to register the user object. If, for example, a registration file named “Roll install.PRG” is included in your package, you should include the line installer = roll install.prg The “folder” keyword may be used to indicate the subfolder of the default user object direc- tory into which you wish to extract the package files. Thus,
- 242. Defining a Registered User Object Class—235 folder = Roll tells EViews to extract the contents of the UOPZ file into the “Roll” folder of the User Object directory. If no folder is specified, the basename of the UOPZ file will be used as the target folder name. Installer Program If you wish to facilitate automatic registration of the user object class, you should create a simple EViews “.PRG” program that uses the adduo command to register the User Object class. For example, the rolling regression user object described in “Rolling Regression Esti- mation Object (Roll)” on page 222 may be registered by including the command adduo(name="roll", version="1.0", desc="Rolling regression object") ./rolldef.ini in a program file, and including a reference to this file in the package table-of-contents. Documentation We recommend that you provide documentation for your user object, and use the “docs=” option to point to the documentation file. Providing some documentation for the command line methods of initializing the object and accessing views and procs is especially important. Documentation could be anything from a simple text file with some syntax hints, to a lengthy PDF document that describes the user object features in detail. Version You may specify an version number for your user object. Version numbers allow users to use automatic updating to ensure they have the latest version of the object class definition files. When a user uses the Update button on the Manage User Objects dialog to check for updates, EViews will compare the hosted version number with the currently registered ver- sion number and download the latest version if newer. You may use the “version=” option to specify the version number. If omitted, EViews will assume that the user object version number is 1.0. XML File Specification One of the most useful user object management features is the ability of users to automati- cally update their installed user object as newer versions become available. To support this feature, EViews must know where to look to determine the most recent version of the user object and where to download any updates. (Note that this specification is identical to the file specification for Add-ins and the material below is virtually identical to the discussion for Add-ins.) This information is communicated in an XML file, typically located on the Add-ins package hosting site. If you will be hosting this file, you should use the adduo option “url=” to spec-
- 243. 236—Chapter 9. User Objects ify the URL for the XML file. If this option is not supplied, EViews will look for the XML file on EViews.com. The XML file should contain one or more item definitions, where each item is a set of infor- mation on a specific user object. The item definition is contained in the lines between an <item> and </item> tag. The full specification of an item is as follows: <item> <title>Roll</title> <version>1.0</version> <description>User Object for performing rolling regression.</ description> <link>http://eviews.com/Addins/Roll.uopz</link> <pubDate>14 Dec 2012</pubDate> </item> The only required specifications are the <title> and <link>. The <version> is used to specify the current user object version number. When the user checks for updates, EViews will download the user object if the version number they have currently installed is lower than the one given in the <version> tag. The <link> specification contains the URL (or network file location) of the UOPZ file con- taining the user object package. This is the location from which EViews will download the updated Add-in package should the user request an update. The <description> and <pubDate> specifications should be self-explanatory. User Object Programming Support EViews offers several programming language features that will aid you in creating, register- ing and working with user objects. Note that there is additional programming language support for creating the programs that will be used to define your user object. These features are described in “Add-ins Design Sup- port” on page 209. Declaration The userobj command is used to create a new, unregistered user object as in userobj myobject where myobject is the name of the object to be created. A userobj created using this com- mand will be empty, and will have no constructor or defined views and procs defined.
- 244. User Object Programming Support—237 To declare a registered user object, you will use the name of the class followed by the name of the object: userobj_class_name(options) myobject [args] Depending on how the user object is designed, the declaration program may use options and additional arguments args when running the constructor program. See Userobj::userobj (p. 817). Registration The adduo command may be used to register a user object, as in adduo(name="roll", version="1.0", desc="Rolling regression object") ./rolldef.ini You may use the command to specify the object definition file, path, description, version number, documentation file, XML file, etc. See adduo (p. 290) for details. View and Procs Each type of user object will have its own views and procs: • All user objects provide a small number of generic built-in views and procs. For exam- ple, the standard EViews label view for viewing and modifying the label contents and the display view for showing output in the object window are supported. • All user objects support the add and drop procs which may be used to populate the user object, and the extract proc may be employed to extract objects into the work- file. • In addition, registered user objects may provide custom views and procs. You should view the specific user object documentation file for details. As with any EViews object, you may access the views and procs of the user object using the object View or Proc menu, or via the command line using the standard syntax: myobject.view_name(options) [args] myobject.proc_name(options) [args] See “User Object Views,” on page 810 and “User Object Procs,” on page 810 for a listing of the built-in views and procs. Data Members The @hasmember(obname) function, available as a data member for all user objects, returns a boolean value depending on whether an object called obname currently exists inside the User Object.
- 245. 238—Chapter 9. User Objects The @members data member returns a space delimited string containing a list of all objects currently inside the user object. See “User Object Data Members,” on page 810.
- 246. Chapter 10. User-Defined Optimization EViews offers a wide variety of built-in estimation methods that involve optimization, including (but not limited to) those supported by the Equation, System, Sspace, and VAR objects. In addition, the EViews Logl object lets you maximize user-defined likelihood functions. While useful in a wide range of settings, the Logl object is nevertheless restricted in the types of functions that it can handle. In particular, the Logl requires that all computations be specified using series expressions, and that the log-likelihood objective can be expressed as a series containing log-likelihood contributions for each observation. In contrast, the optimize (p. 419) command provides tools that allow you to find the opti- mal parameters or control values of a user-defined function. Notably, optimize supports quite general functions so that the computations and the user-defined objective need not be series-based. Defining the Objective and Controls To use optimize, you must first construct an EViews subroutine with arguments to define an output objective which depends on input controls. Recall that a subroutine with arguments is simply a set of commands in a program that can be called one or more times within the program (“Subroutine with Arguments” on page 150). The arguments of the subroutine will correspond to the objective and to inputs that are required to calculate the objective. Each time the subroutine is called, the objective will be computed using the current values of the input controls. The objective, which must be associated with an argument of the subroutine, may be a sca- lar, or may consist of many values stored in an EViews object such as a vector, matrix, or series. The controls, which may be thought of as input parameters, are passed into the subroutine as an argument. As with the objective, the controls may be a scalar value, or a multi-valued object such as a vector, matrix, or series. Note that when series objects are employed as either the objective or control, only the corre- sponding elements in the current workfile sample will be used. optimize will determine the values of the controls that optimize the objective. If the objec- tive is many-valued, EViews will optimize the sum or sum-of-squares of the values, with respect to the control elements.
- 247. 240—Chapter 10. User-Defined Optimization Since the objective is defined using an EViews subroutine, you may optimize almost any- thing that may be computed using EViews commands. Notable, you may use optimize to optimize general functions as well as likelihoods involving matrix computations (neither of which may be optimized using the Logl object). Consider, for example, the simple quadratic function defined as an EViews subroutine: subroutine f(scalar !y, scalar !x) !y = 5*!x^2 - 3*!x - 2 endsub This subroutine has one output and one input, the program variable scalars !X and !Y, respectively. For a given control value for !X, the subroutine computes the value of the sca- lar objective !Y. In its simplest form, a subroutine designed to work with optimize requires only two argu- ments—an objective and control parameters. However you may include additional argu- ments, some of which may be used by optimize, while others are ignored. For example, the subroutine, subroutine SqDev(series out, scalar in, series y) out = (y - in)^2 endsub computes the squared deviations of the argument series Y from the control scalar, and places the element results in the output objective series OUT. The subroutine argument for the series Y will not be used by optimize, but allows optimization to be performed on arbi- trary series without re-coding the subroutine. By default, optimize will assume that the first subroutine argument corresponds to the objective and the second argument corresponds to the controls. As we will see, the default associations may be changed through the use of options in the optimize command (“The Optimize Command” on page 241). Typically, multiple control values are passed into the subroutine in the form of a vector or matrix, as in subroutine local loglike(series logl, vector beta, series dep, group regs) !pi = @acos(-1) series r = dep - beta(1) - beta(2)*regs(1) - beta(3)*regs(2) - beta(4)*regs(3) logl = @log((1/beta(5)*@dnorm(r/beta(5)) endsub where the control vector BETA and the auxiliary arguments for the dependent variable series DEP and the regressors group REGS are used as inputs for the computation of the normal
- 248. The Optimize Command—241 log-likelihood contributions in the objective series LOGL. Note that the first four elements of the vector BETA correspond to the mean regression coefficients, and the last element is the parameter for the standard deviation of the error distribution. Lastly, when designing your subroutine, you should always define the objective to return NA values for bad control values, since returning an arbitrary value may make numeric derivatives unreliable at points close to the invalid region. The Optimize Command The syntax for the optimize command is: optimize(options) subroutine_name(arguments) where subroutine_name is the name of the defined subroutine in your program (or included programs). The full set of options is provided in optimize (p. 419), By default, EViews will assume that the first argument of the subroutine is the objective of the optimization, and that the second argument contains the controls. The default is to max- imize the objective or sum of the objective values (with the sum taken over the current workfile sample, if a series). Specifying the Method and Objective You may control the type of optimization and which subroutine argument corresponds to the objective by providing one of the following options to the optimize command: • max [=integer] • min [=integer] • ls [=integer] • ml [=integer] The four options correspond to different optimization types: maximization (“max”), minimi- zation (“min”), least squares (“ls”) and maximum likelihood (“ml”). If the objective is sca- lar valued only “max” and “min” are allowed. As the names suggest, “min” and “max” correspond to minimizing and maximizing the objective. If the objective is multi-valued, optimize will minimize or maximize the sum of the elements of the objective. “ls” and “ml” are special forms of minimization and maximization that may be specified only if the multi-valued objective argument has a value for each observation. “ls” tells optimize that you wish to perform least squares estimation so the optimizer should mini- mize the sum-of-squares of the elements of the objective. “ml” informs optimize you wish
- 249. 242—Chapter 10. User-Defined Optimization to perform maximum likelihood estimation by maximizing the sum of the elements in the objective. “ls” and “ml” differ from “min” and “max” in supporting an additional option for approxi- mating the Hessian matrix (see “Calculating the Hessian” on page 243) that is used in the estimation algorithm. Indeed the only difference between the “max” and “ml” for a multi- valued objective is that “ml” supports the use of this option (“hess=opg”). By default, the first argument of the subroutine is taken as the objective. However you may specify an alternate objective argument by providing an integer value identifier with one of the options above. For example, to identify the second argument of the subroutine as the objective in a minimization problem, you would use the option “min=2”. Identifying the Control By default, the second argument in the subroutine contains the controls for the optimiza- tion. You may modify this by including the “coef=integer” option in the optimize com- mand, where integer is the argument identifier. For example, to identify the first argument of the subroutine as the control, you would use the option “coef=1”. Starting Values The values of the objects containing the control parameters at the onset of optimization are used as starting values for the optimization process. You should note that if any of the con- trol parameters contain missing values at the onset of optimization, or if the objective func- tion, or any analytic gradients cannot be evaluated at the initial parameter values, EViews will error and the optimization process will terminate. Specifying Gradients If included in the optimize command, the “grad=” option specifies which subroutine argument contains the analytic gradients for each of the coefficients. If you specify the “grad=” option, the subroutine should fill out the elements of the gradient argument with values of the analytical gradients at the current coefficient values. • If the objective argument is a scalar, the gradient argument should be a vector of length equal to the number of elements in the coefficient argument. • If the objective argument is a series, the gradient argument should be a group object containing one series per element of the coefficient argument. The series observations should contain the corresponding derivatives for each observation in the current workfile sample. • For a vector objective, the gradient argument should be a matrix with number of rows equal to the length of the objective vector, and columns equal to the number of ele- ments in the coefficient argument.
- 250. The Optimize Command—243 • “grad=” may not be specified if the objective is a matrix. If “grad=” is not specified, optimize will use numeric gradients. In general, we have found that using numerical gradients performs as well as analytic gradients. Since programming the calculation of the analytic gradients into the subroutine can be complicated, omitting the “grad=” option should usually be one’s initial approach. Calculating the Hessian The “hess=” option tells EViews which Hessian approximation should be used in the esti- mation algorithm. You may employ numeric Hessian (“hess=numeric”), Broyden-Fletcher- Goldfarb-Shanno (“hess=bfgs”), or outer-product of the gradients (“hess=opg”) approxi- mations to the Hessian (see “Hessian Approximation” on page 251). You may not specify an analytic Hessian, though all three approximations use information from the gradients, so that there will be slight differences in the Hessian calculation depend- ing on whether you use numeric versus analytical gradients. The “finalh=” option allows you to save the Hessian matrix of the optimization problem at the final coefficient values as a matrix in the workfile. For least squares and maximum like- lihood problems, the Hessian is commonly used in the calculation of coefficient covariances. For OPG and numeric Hessian approximations, the final Hessian will be the same as the Hessian approximation used during optimization. For BFGS, the final Hessian will be based on the numeric Hessian, since the BFGS approximation need not converge to the true Hes- sian. Numeric Derivatives You can control the method of computing numeric derivatives for gradient or Hessian calcu- lations using the “deriv=” option. At the default setting of “deriv=auto”, EViews will change the number of numeric deriva- tive evaluation points as the optimization routine progresses, switching to a larger number of points as it approaches the optimum. When you include the “deriv=high” option, EViews will always evaluate the objective func- tion at a larger number of points. Iteration and Convergence The “m=” and “c=” options set the maximum number of iterations, and the convergence criterion respectively. Note that for optimization, the number of iterations is the number of successful steps that take place, and that each iteration may involve many function evalua- tions, both to evaluate any required numeric derivatives and for backtracking in cases where a trial step fails to improve the objective.
- 251. 244—Chapter 10. User-Defined Optimization Reaching the maximum number of iterations will cause an error to occur (unless the “noerr” option is set). Advanced Optimization Options There are several advanced options which control different aspects of the optimization pro- cedure. In general, you should not need to worry about these settings, but they may prove useful in cases where you are experiencing estimation difficulties. Trust Region You may use the “trust=” option to set the initial trust region size as a proportion of the ini- tial control values. The default trust region size is 0.25. Smaller values of this parameter may be used to provide a more cautious start to the optimi- zation in cases where larger steps immediately lead into an undesirable region of the objec- tive. Larger values may be used to reduce the iteration count in cases where the objective is well behaved but the initial values may be far from the optimum values. See “Technical Details,” on page 250 for discussion. Step Method optimize offers several methods for determining the constrained step size which you may specify using the “step=” option. In additional to the default Marquardt method (“step=marquardt”), you may specify dogleg steps (“step=dogleg”) or a line-search deter- mined step (“step=linesearch”). Note that in most cases the choice of step method is less important than the selection of Hessian approximation. See “Step Method,” on page 253 for additional detail. Scale By default, the optimization procedure automatically adjusts the scale of the objective and control variables using the square root of the maximum observed value of the second deriv- ative (curvature) of each control parameter. Scaling may be switched off using the “scale=none” option. See “Scaling,” on page 254 for discussion. Objective Accuracy The “feps=” option may be used to specify the expected relative accuracy of the objective function. The default value is 2.2e-16. The value indicates what fraction of the observed objective value should be considered to be random noise. You may wish to increase the “feps=” value if the calculation of your objec- tive may be relatively inaccurate.
- 252. Examples—245 Status Functions To support the optimize command, EViews provides three functions that return informa- tion about the optimization process: • @optstatus provides a status code for the optimizer, both during and post-optimiza- tion. • @optiter returns the current number of iterations performed. If called post-optimiza- tion, it will return the number of iterations required for convergence. • @optmessage returns a one line text message based on status and iteration informa- tion that summarizes the current state of an optimization. All three of these functions may be used during optimization by including them inside the optimization subroutine, or post-optimization by calling them after the optimize com- mand. Error Handling The “noerr” option may be used as an option to suppress any error messages created when the optimization fails. By default, the optimization procedure will generate an error when- ever the results of the optimization appear to be unreliable, such as if convergence was not met, or the gradients are non-zero at the final solution. If noerr is specified, these errors will be suppressed. In this case, your EViews program may still test whether the optimization succeeded using the @optiter function. Note that the noerr option is useful in cases where you are deliberately stopping optimization early using the m= maximum iterations option, since otherwise this will generate an error. Examples We demonstrate the use of the optimize command with several examples. To begin, we consider a regression problem using a workfile created with the following set of commands: wfcreate u 100 rndseed 1 series e = nrnd series x1 = 100*rnd series x2 = 30*nrnd series x3 = -4*rnd group xs x1 x2 x3 series y = 3 + 2*x1 + 4*x2 + 5*x3 + e equation eq1.ls y c x1 x2 x3 These commands create a workfile with 100 observations, and then generate some random data for series X1, X2 and X3, and E (where E is drawn from the standard normal distribu- tion). The series Y is created as 3+2*X1+4*X2+5*X3 + E.
- 253. 246—Chapter 10. User-Defined Optimization To establish a baseline set of results for comparison, we regress Y against a constant, X1, X2, and X3 using the built-in least squares method of the EViews equation object. The results view for the resulting equation EQ1 contains the regression output: Next we use the optimize command with the least squares method to estimate the coeffi- cients in the regression problem. Running a program with the following commands pro- duces the same results as the built-in regression estimator: subroutine leastsquares(series r, vector beta, series dep, group regs) r = dep - beta(1) - beta(2)*regs(1) - beta(3)*regs(2) - beta(4)*regs(3) endsub series LSresid vector(4) LSCoefs lscoefs = 1 optimize(ls=1, finalh=lshess) leastsquares(LSresid, lscoefs, y, xs) scalar sig = @sqrt(@sumsq(LSresid)/(@obs(LSresid)-@rows(LSCoefs))) vector LSSE = @sqrt(@getmaindiagonal(2*sig^2*@inverse(lshess))) We begin by defining the LEASTSQUARES subroutine which computes the regression resid- ual series R, using the parameters given by the vector BETA, the dependent variable given by the series DEP, and the regressors provided by the group REGS. All of these objects are arguments of the subroutine which are passed in when the subroutine is called. Next, we declare the LSRESID series and a vector of coefficients, LSCOEFS, which we arbi- trarily initialize at a value of 1 as starting values.
- 254. Examples—247 The optimize command is called with the “ls” option to indicate that we wish to perform a least squares optimization. The “finalh” option is included so that we save the estimated Hessian matrix in the workfile for use in computing standard errors of the estimates. opti- mize will find the values of LSCOEFS that minimize the sum of squared values of LSRESID as computed using the LEASTSQUARES subroutine. Once optimization is complete, LSCOEFS contains the point estimates of the coefficients. For least squares regression, the standard error of the regression is calculated as the square root of the sum of squares of the residuals, divided by . We store in the scalar SIG. Standard errors may be calculated from the Hessian as the square root of the diagonal of . We store these values in the vector LSSE. The coefficients in LSCOEFS, standard error of the regression in SIG, and coefficient stan- dard errors in LSSE, all match the results in EQ1. Alternately, we may use optimize to estimate the maximum likelihood estimates of the regression model coefficients. Under standard assumptions, an observation-based contribu- tion to the log-likelihood for a regression with normal error terms is of the form: (10.1) The following code obtains the maximum likelihood estimates for this model: subroutine loglike(series logl, vector beta, series dep, group regs) series r = dep - beta(1) - beta(2)*regs(1) - beta(3)*regs(2) - beta(4)*regs(3) logl = @log((1/beta(5))*@dnorm(r/beta(5))) endsub series LL vector(5) MLCoefs MLCoefs = 1 s T k– s 2s 2 H 1– s Lt 1 j 2p -------------- et 2 – 2j 2 ---------- exp log 1 j ---f et j ---- log
- 255. 248—Chapter 10. User-Defined Optimization MLCoefs(5) = 100 optimize(ml=1, finalh=mlhess, hess=numeric) loglike(LL, MLCoefs, y, xs) vector MLSE = @sqrt(@getmaindiagonal(-@inverse(mlhess))) scalar ubsig = mlcoefs(5)*@sqrt(@obs(LL)/(@obs(LL) - @rows(MLCoefs) + 1)) %status = @optmessage statusline {%status} The subroutine LOGLIKE computes the regression residuals using the coefficients in the vec- tor BETA, the dependent variable series given by DEP, and the regressors in the group REGS. Given R, the subroutine evaluates the individual log-likelihood contributions and puts the results in the argument series LOGL. The next lines declare the series LL to hold the likelihood contributions and the coefficient vector BETA to hold the controls. Note that the coefficient vector, BETA, has five elements instead of the four used in least-squares optimization, since we are simultaneously estimat- ing the four regression coefficients and the error standard deviation . We arbitrarily initial- ize the regression coefficients to 1 and the distribution standard deviation to 100. We set the maximizer to perform a maximum likelihood based estimation using the “ml=” option and to store the OPG Hessian in the workfile in the sym objected MLHESS. The coef- ficient standard errors for the maximum likelihood estimates may be calculated as the square root of the main diagonal of the negative of the inverse of MLHESS. We store the esti- mated standard errors in the vector MLSE. Although the regression coefficient estimates match those in the baseline, the ML estimate of in the fifth element of BETA differs. You may obtain the corresponding unbiased esti- mate of sigma by multiplying the ML estimate by multiplying BETA(5) by , which we calculate and store in the scalar UBSIG. j j T T k–
- 256. Examples—249 Note also that we use @optmessage to obtain the status of estimation, whether convergence was achieved and if so, how many iterations were required. The status is reported on the statusline after the optimize estimation is completed. The next example we provide shows the use of the “grads=” option. This example re-calcu- lates the least-squares example above, but provides analytic gradients inside the subroutine. Note that for a linear least squares problem, the derivatives of the objective with respect to the coefficients are the regressors themselves (and a series of ones for the constant): subroutine leastsquareswithgrads(series r, vector beta, group grads, series dep, group regs) r = dep - beta(1) - beta(2)*regs(1) - beta(3)*regs(2) - beta(4)*regs(3) grads(1) = 1 grads(2) = regs(1) grads(3) = regs(2) grads(4) = regs(3) endsub series LSresid vector(4) LSCoefs lscoefs = 1 series grads1 series grads2 series grads3 series grads4 group grads grads1 grads2 grads3 grads4 optimize(ls=1, grads=3) leastsquareswithgrads(LSresid, lscoefs, grads, y, xs) Note that the series for the gradients, and the group containing those series, were declared prior to calling the optimize command, and that the subroutine fills in the values of the series inside the gradient group. Up to this point, our examples have involved the evaluation of series expressions. The opti- mizer does, however, work with other EViews commands. We could, for example, compute the least squares estimates using the optimizer to “solve” the normal equation for . While the optimizer is not a solver, we can trick it into solving that equation by creating a vector of residuals equal to , and asking the opti- mizer to find the values of that minimize the square of those residuals: subroutine local matrixsolve(vector rvec, vector beta, series dep, group regs) stom(regs, xmat) xmat = @hcat(@ones(100), xmat) stom(dep, yvec) rvec = @transpose(xmat)*xmat*beta - @transpose(xmat)*yvec XX b XY b XX b XY– b
- 257. 250—Chapter 10. User-Defined Optimization rvec = @epow(rvec,2) endsub vector(4) MSCoefs MSCoefs = 1 vector(4) rvec optimize(min=1) matrixsolve(rvec, mscoefs, y, xs) Since we will be using matrix manipulation for the objective function, the first few lines of the subroutine convert the input dependent variable series and regressor group into matri- ces. Note that the regressor group does not contain a constant term upon input, so we append a column of ones to the regression matrix XMAT, using the @hcat command. Lastly, we use the optimize command to find the minimum of a simply function of a single variable. We define a subroutine containing the quadratic form, and use the optimize com- mand to find the value that minimizes the function: subroutine f(scalar !y, scalar !x) !y = 5*!x^2 - 3*!x - 2 endsub create u 1 scalar in = 0 scalar out = 0 optimize(min) f(out, in) This example first creates an empty workfile and declares two scalar objects, IN and OUT, for use by the optimizer. IN will be used as the parameter for optimization, and is given an arbitrary starting value of 0. The subroutine F calculates the simple quadratic formula: (10.2) After running this program the value of IN will be 0.3, and the final value of OUT (evaluated at the optimal IN value) is -2.45. As a check we can manually calculate the minimal value of the function by taking derivatives with respect to X, setting equal to zero, and solving for X: (10.3) Technical Details The optimization procedure uses a Newton (or quasi-Newton) based approach to optimiza- tion. In this approach, the first and second derivatives of the objective are used to form a local quadratic approximation to the objective function around the current value of the con- trol parameters. The procedure then calculates the change in the control values that would maximize (or minimize) the objective if the objective function were to exactly follow the local approximation. Y 5X 2 3X– 2– dY dX ------- 10X 3– X 0.3
- 258. Technical Details—251 Mathematically, if the local approximation of the objective around the control values is: (10.4) where is the objective function, is the gradient, and is the Hessian, then the first- order conditions for a maximum give the following expression for the Newton step: (10.5) Note that this local approximation may become quite inaccurate as we move away from the current parameter values. At the full Newton step, the objective may improve by much less than the approximation suggests, or may even worsen. To deal with this possibility, the opti- mization procedure uses a trust region approach (More and Sorensen, 1983). In the trust region approach, the local quadratic approximation is only maximized within a limited neighborhood of the current control values, so that the change in control values at each step is not allowed to exceed a current maximum step size. We then evaluate the objective at the new proposed parameter values. If the local approximation appears to be accurate, the max- imum allowed step size is increased. If the local approximation appears to be inaccurate, the maximum allowed step size is decreased. A step is only accepted when it results in a suffi- ciently large reduction in the objective relative to the reduction that was predicted by the local approximation. Mathematically the constrained step can be written as: (10.6) where is the trust region maximum step size. In the case where the maximum step con- straint is binding, typically the step has a solution (10.7) where is chosen so that | . Note that the Newton approach will work best when the objective can be fitted reasonably well by a local quadratic approximation. This will not be the case if the function is discon- tinuous or has discontinuous first or second derivatives. In these cases, the procedure may be slow to find an optimum, and the final parameter values may end up adjacent to a dis- continuity so that the results will need to be interpreted with caution. Hessian Approximation In the discussion above we assumed that the Hessian matrix of second derivatives of the objective with respect to the control parameters are readily available. In practice these deriv- atives will need to be approximated. The optimize procedure provides three different methods: numeric Hessian, Broyden-Fletcher-Goldfarb-Shanno (BFGS), outer-product of the gradients (OPG). f x x min f p f x g x p 1 2 ---pH x p f g H p H x 1– g x – min f p s.t. p d d p H x lI 1– g x – l p d
- 259. 252—Chapter 10. User-Defined Optimization Numeric Hessian The numeric Hessian approach approximates the Hessian using numeric derivatives. If ana- lytic gradients are provided, the Hessian is based on taking numeric first derivatives of the analytic gradients. If analytic gradients are not provided, the Hessian is based on numeric second derivatives of the objective function. You may specify the use of numeric Hessians by including the option “hess=numeric” option in the optimize command. Note that calculating numeric second derivatives may require many evaluations of the objective function. In the case of numeric second derivatives, each Hessian approximation will require additional evaluations proportional to the square of the number of control parameters in the problem. For a large number of control parameters, this method may be quite slow. Broyden-Fletcher-Goldfarb-Shanno (BFGS) The Broyden-Fletcher-Goldfarb-Shanno (BFGS) method approximates the Hessian using an updating scheme where the previous iteration's approximation to the Hessian is adjusted after each step based on the observed change in the gradients. The BFGS update makes as small a change as possible to the existing Hessian approximation so that it is compatible with the observed change in gradients, while ensuring that the approximation to the Hessian remains positive definite. (See Chapter 9 of Dennis and Schna- bel (1983) for a detailed discussion.) To specify the BFGS method, use the optimize command with the “hess=bfgs” option. BFGS requires fewer objective function evaluations per step than computing a numeric Hes- sian, but may take more iterations to converge. Note that the BFGS approximation need not converge to the true Hessian at the optimized control parameter values, so it cannot be used for calculating the coefficient covariances in statistical problems. Note also that the itera- tions are started from a diagonal approximation to the Hessian. Outer-product of Gradients (OPG) For certain statistical problems, the Hessian can be approximated by a multiple of the sum of the outer products of the gradients (OPG) of individual contributions to the total objective with respect to the coefficients. In the case of least squares problems, this method is com- monly referred to as the Gauss-Newton method. In maximum likelihood settings, this method is often referred to as the BHHH (Berndt, Hall, Hall, and Hausman, 1974) method. In both settings, the approximations are based on the statistical idea that the expected value of the Hessian at the optimized parameter values is equal to a multiple of the expected value of the sum of the outer product of gradients and that the two will converge as the sample
- 260. Technical Details—253 size becomes large. The asymptotic equivalence implies that these OPG approximations will be closer to the true Hessian when working with medium to large sample sizes and when coefficients are close to the true coefficient values. You may select the OPG approximation using the “hess=opg” option. Note that the OPG method may only be used when the objective is a set of least squares residuals (specified using the “ls” option) or a set of maximum likelihood contributions (specified using the “ml” option), since there is no reason to believe the approximation is valid for an arbitrary maximization or minimization objective. OPG uses the same number of objective evaluations per step as BFGS, which is less than the number required for evaluating the numeric Hessian. Step Method Different step methods are supported by optimize, with each following a trust region approach, where the full Newton step is taken whenever the step is less than the current maximum step size, and a constrained step is taken when the full Newton step exceeds the current maximum step size. The methods differ in how the constrained step is taken. Note that in most cases, the choice of step method is less important than the selection of Hessian approximation. Marquardt The default Marquardt option closely follows the method outlined above where the con- strained step is calculated by an iterative procedure that searches for a diagonal adjustment to the Hessian that makes the step size equal to the maximum allowed step size. The Mar- quardt step has the highest computational cost, although since for most statistical estima- tion most computation time is spent evaluating the objective rather than calculating an optimal step, this is unlikely to matter unless the number of controls is fairly large and the objective can be evaluated cheaply. Dogleg The dogleg method is a cheaper approximation to the trust region problem where the con- strained step is calculated by combining a Newton step with a Cauchy step (a step in the direction of the scaled gradients that minimizes the local quadratic approximation to the objective). For both the Marquardt and dogleg steps, the direction of the step shifts away from the direction of the Newton step towards the direction of steepest descent as the trust region contracts, but the dogleg step uses a simple linear combination of the two steps to achieve this. When the dogleg step is used with a BFGS Hessian (the hess=bfgs option) approximation, the calculations required per iteration are proportional to the square rather than the cube of the number of parameters. This makes the dogleg step attractive if the number of control variables is very large and the objective can be evaluated cheaply.
- 261. 254—Chapter 10. User-Defined Optimization Line-search The line-search method is the simplest approach in which the constrained step is formed by proportionally scaling down the Newton step until it satisfies the maximum step size con- straint. With this method, only the length of the step is changed as the trust region con- tracts, but not its direction. The line-search method is the cheapest method in terms of calculational cost but may be less robust, particularly when used with poor initial values. Note that for both the dogleg and line-search algorithms, an adjustment will be made to the diagonal of the Hessian to ensure positive definiteness before calculating the Newton step. There is also special handling for non-positive definite matrices in the Marquardt step fol- lowing the method outlined in More and Sorensen (1983). Scaling The Newton step is theoretically invariant to both the scale of the objective and the scale of the control variables since any changes to the gradients and the Hessian cancel each other out in the expression for the Newton step. In practice, numerical issues may cause the equivalence to be inexact. Additionally, the constrained trust region steps do not have the invariance property unless scaling is applied to the control variables when calculating a con- strained step. By default, the optimization procedure scales automatically using the square root of the maximum observed value of the second derivative (curvature) of each control parameter. This makes the procedure theoretically invariant to the scaling of the variables. In most cases you should leave the default scaling turned on, but in cases where the Hessian approximation may be unreliable, scaling may be switched off using the “scale=none” option. When scaling is switched off, you may wish to define your objective so that equal size changes to each control variable will have a similar order of magnitude of impact on the objective. Optimization Termination The optimization process will terminate immediately if the initial control parameters contain missing values, the objective function, or if provided, the analytical gradients cannot be evaluated at the starting parameter values. Once the optimization procedure begins, it will proceed even if numerical errors (such as taking the log of a negative number) prevent the objective function from being evaluated at a trial step. An objective with missing values will be taken as indicating that the control val- ues are invalid, and the optimization will step back from the problematic values. Note that you should always define the objective to return NA values for bad control values since returning an arbitrary value may make numeric derivatives unreliable at points close to the invalid region.
- 262. Technical Details—255 The optimization procedure will terminate when: • An unconstrained Newton step improved the objective and the length of the step was less than the specified convergence tolerance. • A constrained step failed to improve the objective and the maximum allowed step size for the next iteration was decreased to become less than the specified convergence tol- erance. • The maximum number of iterations (successful steps) was reached without one of the above criteria being met. When the procedure terminates for a condition other than the maximum iterations being reached, the procedure checks the gradients and curvature of the objective to see whether the first and second order conditions for an optimum appear to be satisfied. If the conditions are not met, the optimization will be considered to have failed. There are a variety of rea- sons that failure may occur: • The objective may have no optimum value, but just gradually flatten out as a control variable becomes very large or small. • The objective may not be defined for some values of the control parameters but may improve as we approach these values. This will cause the optimization to stall with control variables very close to the invalid region, but with non-zero gradients at the final control values. • There may be values for some controls which make other controls included in the optimization have little or no impact on the objective, so that both the gradients and the elements of the Hessian corresponding to the variables gradually become zero as the optimization progresses. • The control variables may 'collapse' so that two or more controls are serving the same role in the objective and their individual effect cannot be separated. This will result in a Hessian that is numerically singular since changes in one control can be exactly off- set by changes in one or more of the other controls without changing the objective. (For statistical problems, this implies that the coefficients are unidentified). In all these cases, a useful approach is to carefully consider starting values so that the initial values for the controls are as close as possible to what you believe the optimum values might be. You should also avoid starting values that are close to any regions in which the objective function cannot be evaluated. If the optimization continues to report problems from a wide range of starting values, this may indicate that your optimization problem is not well defined. Successful convergence does not guarantee that the optimization procedure has found the global optimum of the function. The optimization procedure only tests whether the final point appears to satisfy the conditions necessary for a local optimum. In cases where more
- 263. 256—Chapter 10. User-Defined Optimization than one local optimum may exist, the optimization procedure may converge to different final values depending on what starting values are used. Note that when the optimization completes successfully (no error is reported) the last call to the subroutine that calculates the objective will always be with the control parameters set to the optimized values. (An additional final call to the subroutine will be made in situations where this is not already the case). This guarantees that any intermediate results saved inside the subroutine will also be left at their optimized results after the optimization is com- plete. References Berndt, E., Hall, B., Hall, R., and Hausman, J. (1974). Estimation and Inference in Nonlinear Structural Models, Annals of Economic and Social Measurement, Vol. 3, 653–665. Dennis, J. E. and R. B. Schnabel (1983). “Secant Methods for Systems of Nonlinear Equations,” Numerical Methods for Unconstrained Optimization and Nonlinear Equations. Prentice-Hall, London. More and Sorensen (1983). Computing a Trust Region Step, SIAM Journal of Scientific Statistical Comput- ing, Vol. 4, 553–572.
- 264. Chapter 11. Matrix Language EViews provides you with tools for working directly with data contained in matrices and vectors. You can use the EViews matrix language to perform calculations that are not avail- able using the built-in views and procedures. The following objects may be created and manipulated using the matrix command lan- guage: • matrix: two-dimensional array. • vector: column vector. • sym: symmetric matrix (stored in lower triangular form). • scalar: scalar. • rowvector: row vector. • coef: column vector of coefficients to be used by equation, system, pool, logl, and sspace objects. We term these objects matrix objects (despite the fact that some of these objects are not matrices). Declaring Matrix Objects You must declare a matrix object for it to exist in the workfile. A listing of the declaration statements for the various matrix objects is provided in “Object Creation Commands” on page 281. Briefly, a matrix object declaration consists of the object keyword, along with size informa- tion in parentheses and the name to be given to the object, followed (optionally) by an assignment statement. If no assignment is provided, the object will be initialized to have all zero values. The various matrix objects require different sizing information. A matrix requires the num- ber of rows and the number of columns. A sym requires that you specify a single number representing both the number of rows and the number of columns. A vector, rowvector, or coef declaration can include information about the number of elements. A scalar requires no size information. If size information is not provided, EViews will assume that there is only one element in the object. For example: matrix(3,10) xdata sym(9) moments
- 265. 258—Chapter 11. Matrix Language vector(11) betas rowvector(5) xob creates a matrix XDATA, a symmetric matrix MOMENTS, an column vector BETAS, and a rowvector XOB. All of these objects are initialized to zero. One common operation, creating and filling a vector in one-step, may be performed using the @fill (p. 673) function, as in vector v = @fill(1,4,6,21.3) which returns a 4 element vector, where the first element is set to 1, the second to 4, the third to 6 and the fourth to 21.3. To change the size of a matrix object, you may repeat the declaration statement. Further- more, if you use an assignment statement with an existing matrix object, the target will be resized as necessary. For example: sym(10) bigz matrix zdata matrix(10,2) zdata zdata = bigz will first declare ZDATA to be a matrix with a single element, and then redeclare ZDATA to be a matrix. The assignment statement in the last line will resize ZDATA so that it contains the contents of the symmetric matrix BIGZ. Assigning Matrix Values There are three ways to assign values to the elements of a matrix: you may assign values to specific matrix elements, you may fill the matrix using a list of values, or you may perform matrix assignment. Element assignment The most basic method of assigning matrix values is to assign a value for a specific row and column element of the matrix. Simply enter the matrix name, followed by the row and col- umn indices, in parentheses, and then an assignment to a scalar value. For example, suppose we declare the matrix A: matrix(2,2) a The first command creates and initializes the matrix A so that it contains all zeros. Then after entering the two commands: a(1,1) = 1 a(2,1) = 4 3 10 9 9 11 1 1 5 10 2 10 10 2 2 2 2
- 266. Assigning Matrix Values—259 we have . (11.1) You can perform a large number of element assignments by placing them inside of program- ming loops: vector(10) y matrix (10,10) x for !i = 1 to 10 y(!i) = !i for !j = 1 to 10 x(!i,!j) = !i + !j next next Note that the fill procedure provides an alternative to using loops for assignment (see, for example, the matrix object version of the procedure, Matrix::fill). Fill assignment The second assignment method is to use the fill object procedure to assign a list of num- bers to each element of the matrix in the specified order. By default, the procedure fills the matrix column by column, but you may override this behavior to fill by rows. You should enter the name of the matrix object, followed by a period, the fill keyword, and then a comma delimited list of values. For example, the commands: vector(3) v v1.fill 0.1, 0.2, 0.3 matrix(2,4) x matrix.fill 1, 2, 3, 4, 5, 6, 7, 8 create the matrix objects: (11.2) If we replace the last line with matrix.fill(b=r) 1,2,3,4,5,6,7,8 then X is given by: A 1 0 4 0 V 0.1 0.2 0.3 , X 1 3 5 7 2 4 6 8
- 267. 260—Chapter 11. Matrix Language . (11.3) In some situations, you may wish to repeat the assignment over a list of values. You may use the “l” option to fill the matrix by repeatedly looping through the listed numbers until the matrix elements are exhausted. Thus, matrix(3,3) y y.fill(l) 1, 0, -1 creates the matrix: (11.4) See Matrix::fill for a complete description of the fill procedure for a matrix. Equivalent procedures are available for the remaining matrix objects. Matrix assignment You can copy data from one matrix object into another using assignment statements. To per- form an assignment, you should enter the name of the target matrix followed by the equal sign “=”, and then a matrix object expression. The expression on the right-hand side should either be a numerical constant, a matrix object, or an expression that returns a matrix object. There are rules for how EViews performs the assignment which vary depending upon the types of objects involved in the assignment. Scalar values on the right-hand side If there is a scalar on the right-hand side of the assignment, every element of the matrix object is assigned the value of the scalar. Examples: matrix(5,8) first scalar second vec(10) third first = 5 second = c(2) third = first(3,5) Since declaration statements allow for initialization, you can combine the declaration and assignment statements. Examples: X 1 2 3 4 5 6 7 8 Y 1 1 1 0 0 0 1– 1– 1–
- 268. Copying Data Between Objects—261 matrix(5,8) first = 5 scalar second = c(2) vec(10) third = first(3,5) Same object type on right-hand side If the source object on the right is a matrix or vector, and the target or destination object on the left is of the same type, the target will be resized to have the same dimension as the source, and every source element will be copied. For example: matrix(10,2) zdata = 5 matrix ydata = zdata matrix(10,10) xdata = ydata declares that ZDATA is a matrix filled with 5’s. In the second line, YDATA is auto- matically resized to be a matrix and is filled with the contents of ZDATA. The third line declares and initializes XDATA. Note that even though the declaration of XDATA calls for a matrix, XDATA is a matrix of 5’s. This behavior occurs because the declaration statement above is equivalent to issuing the two commands: matrix(10,10) xdata xdata = ydata which will first declare the matrix XDATA, and then automatically resize it to when you fill it with the values for YDATA (see also “Copying Data From Matrix Objects” on page 262). The matrix object on the right hand side of the declaration statement may also be the output from a matrix function or expression. For example, sym eye4 = @identity(4) declares the symmetric matrix EYE4 which is equal to the identity matrix, while vector b = @inverse(xx)*xy inverts the matrix XX, multiplies it by XY, and assigns the value to the new vector B. The next section discusses assignment statements in the more general case, where you are converting between object types. In some cases, the conversion is automatic; in other cases, EViews provides you with additional tools to perform the conversion. Copying Data Between Objects In addition to the basic assignment statements described in the previous section, EViews provides you with a large set of tools for copying data to and from matrix objects. 10 2 10 2 10 10 10 2 10 10 10 2 4 4
- 269. 262—Chapter 11. Matrix Language At times, you may wish to move data between different types of matrix objects. For example, you may wish to take the data from a vector and put it in a matrix. EViews has a number of built-in rules which make these conversions automatically. At other times, you may wish to move data between a matrix object and an EViews series or group object. There are a separate set of tools which allow you to convert data across a vari- ety of object types. Copying Data From Matrix Objects Data may be moved between different types of matrix objects using assignment statements. If possible, EViews will resize the target object so that it contains the same information as the object on the right side of the equation. The basic rules governing expressions of the form “Y=X” may be summarized as follows: • The object type of the target Y cannot change. • The target object Y will, if possible, be resized to match the object X; otherwise, EViews will issue an error. Thus, assigning a vector to a matrix will resize the matrix, but assigning a matrix to a vector will generate an error if the matrix has more than one column. • The data in X will be copied to Y. Specific exceptions to the rules given above are: • If X is a scalar, Y will keep its original size and will be filled with the value of X. • If X and Y are both vector or rowvector objects, Y will be changed to the same type as X. “Summary of Automatic Resizing of Matrix Objects” on page 278 contains a complete sum- mary of the conversion rules for matrix objects. Here are some simple examples illustrating the rules for matrix assignment: vector(3) x x(1) = 1 x(2) = 2 x(3) = 3 vector y = x matrix z = x Y is now a 3 element vector because it has the same dimension and values as X. EViews automatically resizes the Z Matrix to conform to the dimensions of X so that Z is now a matrix containing the contents of X: Z(1,1)=1, Z(2,1)=2, Z(3,1)=3. Here are some further examples where automatic resizing is allowed: 3 1
- 270. Copying Data Between Objects—263 vector(7) y = 2 scalar value = 4 matrix(10,10) w = value w = y matrix(2,3) x = 1 rowvector(10) t = 100 x = t W is declared as a matrix of 4’s, but it is then reset to be a matrix of 2’s. X is a matrix of 100’s. Lastly, consider the commands: vector(7) y = 2 rowvector(12) z = 3 coef(20) beta y = z z = beta Y will be a rowvector of length 3, containing the original contents of Z, and Z will be a col- umn vector of length 20 containing the contents of BETA. There are some cases where EViews will be unable to perform the specified assignment because the resize operation is ill defined. For example, suppose that X is a matrix. Then the assignment statement: vector(7) y = x will result in an error. EViews cannot change Y from a vector to a matrix and there is no way to assign directly the 4 elements of the matrix X to the vector Y. Other examples of invalid assignment statements involve assigning matrix objects to scalars or sym objects to vector objects. (In may be possible, however, to use the @vec (p. 699) or @vech (p. 699) functions to per- form some of these operations.) Copying Data From Parts Of Matrix Objects In addition to the standard rules for conversion of data between objects, EViews provides matrix functions for extracting from and assigning to parts of matrix objects. Matrix func- tions are described in greater detail later in this chapter. For now, note that some functions take a matrix object and perhaps other arguments and return a matrix object. A comprehensive list of the EViews commands and functions that may be used for matrix object conversion appears in “Matrix Command and Function Summary” on page 653. Here, 10 10 7 1 1 10 2 2
- 271. 264—Chapter 11. Matrix Language we consider a few examples that should provide you with a sense of the types of operations that may be performed. Suppose first that you are interested in copying data from a matrix into a vector. The follow- ing commands will copy data from M1 and SYM1 into the vectors V1, V2, V3, and V4. matrix(10, 10) m1 sym(10) sym1 vector v1 = @vec(m1) vector v2 = @columnextract(m1,3) vector v3 = @rowextract(m1,4) vector v4 = @columnextract(sym1,5) The @vec function creates a 100 element vector, V1, from the columns of M1 stacked one on top of another. V2 will be a 10 element vector containing the contents of the third column of M1 while V3 will be a 10 element vector containing the fourth row of M1. The @vec (p. 699), @vech (p. 699), @rowextract (p. 689), and @columnextract (p. 662) functions also work with sym objects. V4 is a 10 element vector containing the fifth column of SYM1. In some cases, it may be easier to take a subset of the elements of a matrix object using its data member functions. A subset of rows or columns of a matrix may be obtained using the @row, @col, @droprow, or @dropcol object data members. For example, vector a = x.@row(3) extracts the third row of X into the vector a. Similarly, vector b = x.@col(2) puts the second column of X into B. You can also copy data from one matrix into a smaller matrix using @subextract (p. 694). For example: matrix(20,20) m1=1 matrix m2 = @subextract(m1,5,5,10,7) matrix m3 = @subextract(m1,5,10) matrix m4 = m1 M2 is a matrix containing a submatrix of M1 defined by taking the part of the matrix M1 beginning at row 5 and column 5, and ending at row 10 and column 7. M3 is the matrix taken from M1 at row 5 and column 10 to the last element of the matrix (row 20 and column 20). In contrast, M4 is defined to be an exact copy of the full matrix. You may use the data member functions to perform similar operations. For example, matrix xsub = x.@row(@fill(1, 3, 4)) 6 3 16 11 20 20
- 272. Copying Data Between Objects—265 extracts the first, third, and fourth rows of the matrix X into the matrix XSUB. Data from a matrix may be copied into another matrix object using the commands col- place (p. 662), rowplace (p. 690), and matplace (p. 681). Consider the commands: matrix(100,5) m1 = 0 matrix(100,2) m2 = 1 vector(100) v1 = 3 rowvector(100) v2 = 4 matplace(m1,m2,1,3) colplace(m1,v1,3) rowplace(m1,v2,80) The matplace command places M2 in M1 beginning at row 1 and column 3. V1 is placed in column 3 of M1, while V2 is placed in row 80 of M1. You may combine matplace with @fill (p. 673) and the @row, @col, @droprow, or @dropcol data members to perform complex subsetting and filling matplace(z, x.@row(@fill(1, 3, 4)), 1, 1) puts rows 1, 3, and 4 of the matrix X into the upper-left hand corner of Z. Note that Z must be large enough to hold the X subset. Copying Data Between Matrix And Other Objects The previous sections described techniques for copying data between matrix objects such as vectors, matrices and scalars. In this section, we describe techniques for copying data between matrix objects and workfile-based EViews objects such as series and groups. Keep in mind that there are two primary differences between the ordinary series or group objects and the matrix objects. First, operations involving series and groups use information about the current workfile sample, while matrix objects do not. Second, there are important differences in the handling of missing values (NAs) between the two types of objects. Direct Assignment The easiest method to copy data from series or group objects to a matrix object is to use direct assignment. Place the destination matrix object on the left side of an equal sign, and place the series or group to be converted on the right. If you use a series object on the right-hand side and a vector on the left, EViews will only use observations from the current sample to make the vector. If you place a group object on the right and a matrix on the left, EViews will create a rectangular matrix out of the group using observations from the current sample.
- 273. 266—Chapter 11. Matrix Language While direct assignment is straightforward and convenient, there are two principal limita- tions to the approach. First, EViews uses only the observations in the current sample when copying the data. Second, observations containing missing data (NAs) for a series, or for any series in the group, are dropped. Thus, if the current sample contains 20 observations, but the series or group contains missing data, the dimension of the output vector or matrix will be less than 20. (Below, we describe methods which allow you to override the current sample and to retain missing values.) Examples: smpl 1963m03 1993m06 fetch hsf gmpyq group mygrp hsf gmpyq vector xvec = gmpyq matrix xmat = mygrp These statements create the vector XVEC and the two column matrix XMAT containing the non-missing series and group data from 1963M03 to 1993M06. Note that if GMPYQ has a missing value in 1970M01, and HSF contains a missing value in 1980M01, both observations for both series will be excluded from XMAT. When performing matrix assignment, you may refer to an element of a series, just as you would refer to an element of a vector, by placing an index value in parentheses after the name. An index value i refers to the i-th element of the series from the beginning of the workfile range, not the current sample. For example, if the range of the current annual work- file is 1961 to 1980, the expression GNP(6) refers to the 1966 value of GNP. These series ele- ment expressions may be used in assigning specific series values to matrix elements, or to assign matrix values to a specific series element. For example: matrix(5,10) x series yser = nrnd x(1,1) = yser(4) yser(5) = x(2,3) yser(6) = 4000.2 assigns the fourth value of the series YSER to X(1,1), and assigns to the fifth and sixth val- ues of YSER, the X(2,3) value and the scalar value “4000.2”, respectively. While matrix assignments allow you to refer to elements of series as though they were ele- ments of vectors, you cannot generally use series in place of vectors. Most vector and matrix operations will error if you use a series in place of a vector. For example, you cannot perform a rowplace command using a series name. Furthermore, note that when you are not performing matrix assignment, a series name fol- lowed by a number in parentheses will indicate that the lag/lead operator be applied to the
- 274. Copying Data Between Objects—267 entire series. Thus, when used in generating series or in an equation, system, or model spec- ification, GNP(6) refers to the sixth lead of the GNP series. To refer to specific elements of the GNP series in these settings, you should use the @elem function. Copy using @convert The @convert (p. 664) function takes a series or group object and, optionally, a sample object, and returns a vector or rectangular matrix. If no sample is provided, @convert will use the workfile sample. The sample determines which series elements are included in the matrix. Example: smpl 61 90 group groupx inv gdp m1 vector v = @convert(gdp) matrix x = @convert(groupx) X is a matrix with the first column containing data from INV, the second column from GDP, and the third column from M1. As with direct assignment, @convert excludes observations for which the series or any of the series in the group contain missing data. If, in the example above, INV contains missing observations in 1970 and 1980, V would be a 29 element vector while X would be a matrix. This will cause errors in subsequent operations that require V and X to have a common row dimension. There are two primary advantages of using @convert over direct assignment. First, since @convert is a function, it may be used in the middle of a matrix expression. Second, an optional second argument allows you to specify a sample to be used in conversion. For example: sample s1.set 1950 1990 matrix x = @convert(grp, s1) sym y = @inverse(@inner(@convert(grp, s1))) performs the conversion using the sample defined in S1. Copy using Commands EViews also provides three useful commands that perform explicit conversions between series and matrices with control over both the sample and the handling of NAs. stom (p. 692) (Series TO Matrix) takes a series or group object and copies its data to a vec- tor or matrix using either the current workfile sample, or the optionally specified sample. As with direct assignment, the stom command excludes observations for which the series or any of the series in the group contain missing data. Example: 30 3 28 3
- 275. 268—Chapter 11. Matrix Language sample smpl_cnvrt.set 1950 1995 smpl 1961 1990 group group1 gnp gdp money vector(46) vec1 matrix(3,30) mat1 stom(gdp, vec1, smpl_cnvrt) stom(group1, mat1) While the operation of stom is similar to @convert, stom is a command and cannot be included in a matrix expression. Furthermore, unlike @convert, the destination matrix or vector must already exist and have the proper dimension. stomna (p. 693) (Series TO Matrix with NAs) works identically to stom, but does not exclude observations for which there are missing values. The elements of the series for the relevant sample will map directly into the target vector or matrix. Thus, smpl 1951 2000 vector(50) gvector stom(gdp, gvector) will always create a 50 element vector GVECTOR that contains the values of GDP from 1951 to 2000, including observations with NAs. mtos (p. 683) (Matrix TO Series) takes a matrix or vector and copies its data into an existing series or group, using the current workfile sample or a sample that you provide. Example: mtos(mat1, group1) mtos(vec1, resid) mtos(mat2, group1, smpl1) As with stom the destination dimension given by the sample must match that of the source vector or matrix. Matrix Expressions A matrix expression is an expression which combines matrix objects with mathematical operators or relations, functions, and parentheses. While we discuss matrix functions in great detail below, some examples will demonstrate the relevant concepts. Examples: @inner(@convert(grp, s1)) mat1*vec1 @inverse(mat1+mat2)*vec1
- 276. Matrix Expressions—269 mat1 > mat2 EViews uses the following rules to determine the order in which the expression will be eval- uated: • You may nest any number of pairs of parentheses to clarify the order of operations in a matrix expression. • If you do not use parentheses, the operations are applied in the following order: 1. Unary negation operator and functions. 2. Multiplication and division operators. 3. Addition and subtraction operators. 4. Comparison operators: “>=”, “>”, “<=”, “<”, “<>”. Examples: @inverse(mat1+mat2)+@inverse(mat3+mat4) vec1*@inverse(mat1+mat2)*@transpose(vec1) In the first example, the matrices MAT1 and MAT2 will be added and then inverted. Simi- larly the matrices MAT3 and MAT4 are added and then inverted. Finally, the two inverses will be added together. In the second example, EViews first inverts MAT1+MAT2 and uses the result to calculate a quadratic form with VEC1. Matrix Operators EViews provides standard mathematical operators for matrix objects. (Note that element multiplication, division, inverse, and powers are not available using operators, but are instead supported via functions). Negation (–) The unary minus changes the sign of every element of a matrix object, yielding a matrix or vector of the same dimension. Example: matrix jneg = -jpos Addition (+) You can add two matrix objects of the same type and size. The result is a matrix object of the same type and size. Example: matrix(3,4) a matrix(3,4) b matrix sum = a + b
- 277. 270—Chapter 11. Matrix Language You can add a square matrix and a sym of the same dimension. The upper triangle of the sym is taken to be equal to the lower triangle. Adding a scalar to a matrix object adds the scalar value to each element of the matrix or vector object. Subtraction (–) The rules for subtraction are the same as the rules for addition. Example: matrix(3,4) a matrix(3,4) b matrix dif = a - b Subtracting a scalar object from a matrix object subtracts the scalar value from every ele- ment of the matrix object. Multiplication (*) You can multiply two matrix objects if the number of columns of the first matrix is equal to the number of rows of the second matrix. Example: matrix(5,9) a matrix(9,22) b matrix prod = a * b In this example, PROD will have 5 rows and 22 columns. One or both of the matrix objects can be a sym. Note that the product of two sym objects is a matrix, not a sym. The @inner function will produce a sym by multiplying a matrix by its own transpose. You can premultiply a matrix or a sym by a vector if the number of columns of the matrix is the same as the number of elements of the vector. The result is a vector whose dimension is equal to the number of rows of the matrix. Example: matrix(5,9) mat vector(9) vec vector res = mat * vec In this example, RES will have 5 elements. You can premultiply a rowvector by a matrix or a sym if the number of elements of the rowvector is the same as the number of rows of the matrix. The result is a rowvector whose dimension is equal to the number of columns of the matrix. Example:
- 278. Matrix Expressions—271 rowvector rres matrix(5,9) mat rowvector(5) row rres = row * mat In this example, RRES will have 9 elements. You can multiply a matrix object by a scalar. Each element of the original matrix is multi- plied by the scalar. The result is a matrix object of the same type and dimensions as the orig- inal matrix. The scalar can come before or after the matrix object. Examples: matrix prod = 3.14159*orig matrix xxx = d_mat*7 To perform element multiplication where you multiply every element of a matrix by very element of another matrix, you should use the @emult (p. 671) function. Division (/) You can divide a matrix object by a scalar. Example: matrix z = orig/3 Each element of the object ORIG will be divided by 3. To perform element division where you divide every element of a matrix by very element of another matrix, you should use the @ediv (p. 667) function. Relational Operators (=, >, >=, <, <=, <>) Two matrix objects of the same type and size may be compared using the comparison oper- ators (=, >, >=, <, <=, <>). The result is a scalar logical value. Every pair of corre- sponding elements is tested, and if any pair fails the test, the value 0 (FALSE) is returned; otherwise, the value 1 (TRUE) is returned. For example, if result <> value then run crect endif It is possible for a vector to be not greater than, not less than, and not equal to a second vec- tor. For example: vector(2) v1 vector(2) v2 v1(1) = 1 v1(2) = 2 v2(1) = 2
- 279. 272—Chapter 11. Matrix Language v2(2) = 1 Since the first element of V1 is smaller than the first element of V2, V1 is not greater than V2. Since the second element of V1 is larger than the second element of V2, V1 is not less than V2. The two vectors are not equal. Matrix Commands and Functions EViews provides a number of commands and functions that allow you to work with the con- tents of your matrix objects. These commands and functions may be divided into roughly four distinct types: (1) utility commands and functions, (2) element functions, (3) matrix algebra functions, and (4) descriptive statistics functions. Utility Commands and Functions The utility commands and functions provide support for creating, manipulating, and assign- ing values to your matrix objects. We have already seen a number of these commands and functions, including the @convert (p. 664) function and the stom (p. 692) command, both of which convert data from series and groups into vectors and matrices, as well as @vec (p. 699), @vech (p. 699), @rowextract (p. 689), @columnextract (p. 662), and mat- place (p. 681). A random sampling of other useful commands and functions include: matrix a = @ones(10, 5) which creates a matrix of ones, vector f = @getmaindiagonal(x) which extracts the main diagonal from the square matrix X, matrix g = @explode(sym01) which creates a square matrix from the symmetric matrix object SYM01, and matrix h1 = @resample(y) matrix h2 = @permute(y) which create matrices by randomly drawing (with replacement) from, and by permuting, the rows of Y. A full listing of the matrix commands and functions is included in the matrix summary on “Matrix Command and Function Summary” on page 653. Element Functions EViews offers two types of functions that work with individual elements of a matrix object. First, most of the element functions that may be used in series expressions can used with 10 5
- 280. Matrix Commands and Functions—273 matrix objects. When used with a matrix object, these functions will return a similar object whose elements are the values of a function evaluated at each element of the original. For example, you may specify matrix f = @log(y) to compute the logarithm of each element of the matrix Y. Similarly, matrix tprob = @ctdist(x, df) evaluates the cumulative distribution function value for the t-distribution for each element of the matrix X and places the results in the corresponding cells of the matrix TPROB. Note that DF may either be a scalar, or a matrix object of the same type and size as X. (See “Basic Mathematical Functions” on page 549, “Special Functions” on page 566, “Trigo- nometric Functions” on page 569, and “Statistical Distribution Functions” on page 569 for summaries of the various element functions.) Second, EViews provides a set of element functions for performing element-by-element matrix multiplication, division, inversion, and exponentiation. For example, to obtain a matrix Z containing the inverse of every element in X, you may use: matrix z = @einv(x) Likewise, to compute the elementwise (Hadamard) product of the matrices A and B, you may use matrix ab = @emult(a, b) The (i,j)-th element of the matrix AB will contain the product of the corresponding elements of A and B: . See “Matrix Element Functions” on page 655 for details. Matrix Algebra Functions The matrix algebra functions allow you to perform common matrix algebra operations. Among other things, you can use these routines to compute eigenvalues, eigenvectors and determinants of matrices, to invert matrices, to solve linear systems of equations, and to per- form singular value decompositions. For example, to compute the inner product of two vectors A and B, you may use scalar ip = @inner(a, b) To compute the Cholesky factorization of a symmetric matrix G, matrix cf = @cholesky(g) The least squares coefficient vector for the data matrix X and vector Y may be computed as aij bij
- 281. 274—Chapter 11. Matrix Language vector b= @inverse(@inner(x))*@transpose(x)*y A listing of the matrix algebra functions and commands is provided in “Matrix Algebra Functions” on page 655. Descriptive Statistics Functions The descriptive statistics functions compute summary statistics for the data in the matrix object. You can compute statistics such as the mean, median, minimum, maximum, and variance, over all of the elements in your matrix. For example, scalar xmean = @mean(xmat) computes the mean taken over all of the non-missing elements of the matrix XMAT, and assigns the values to the scalar XMEAN. Similarly, the commands scalar xquant95 = @quantile(xmat, .95) computes the .95 quantile of the elements in XMAT. Functions for computing descriptive statistics are discussed in “Descriptive Statistics” on page 552. In addition, there are functions for computing statistics for each column in a matrix. vector xmeans = @cmean(xmat) computes the mean for each column of XMAT and assigns the values to the vector XMEANS. vector xmin = @cmin(xmat) saves the column minimums in the vector XMIN. If, instead you wish to find the index of the minimum element for the column, you may use @cimin instead: vector ximin = @cimin(xmat) The column statistics are outlined in “Matrix Column Functions” on page 656. Functions versus Commands A function generally takes arguments, and always returns a result. Functions are easily iden- tified by the initial “@” character in the function name. There are two basic ways that you can use a function. First, you may assign the result to an EViews object. This object may then be used in other EViews expressions, providing you with access to the result in subsequent calculations. For example: matrix y = @transpose(x) stores the transpose of matrix X in the matrix Y. Since Y is a standard EViews matrix, it may be used in all of the usual expressions.
- 282. Matrix Commands and Functions—275 Second, you may use a function as part of a matrix expression. Since the function result is used in-line, it will not be assigned to a named object, and will not be available for further use. For example, the command: scalar z = vec1*@inverse(v1+v2)*@transpose(vec1) uses the results of the @inverse and @transpose functions in forming the scalar expres- sion assigned to Z. These function results will not be available for subsequent computations. By contrast, a command takes object names and expressions as arguments, and operates on the named objects. Commands do not return a value. Commands, which do not have a leading “@” character, must be issued alone on a line, and may not be used as part of a matrix expression. For example, to convert a series X to a vector V1, you would enter: stom(x, v1) Because the command does not return any values, it may not be used in a matrix expres- sion. NA Handling As noted above, most of the methods of moving data from series and groups into matrix objects will automatically drop observations containing missing values. It is still possible, however, to encounter matrices which contain missing values. For example, the automatic NA removal may be overridden using the stomna command. Additionally, some of the element operators may generate missing values as a result of stan- dard matrix operations. For example, taking element-by-element logarithms of a matrix using @log will generate NAs for all cells containing nonpositive values. EViews follows two simple rules for handling matrices that contain NAs. For all operators, commands, and functions (with the exception of the descriptive statistics functions), EViews works with the full matrix object, processing NAs as required. For descriptive statistic func- tions, EViews automatically drops NAs when performing the calculation. These rules imply the following: • Matrix operators will generate NAs where appropriate. Adding together two matrices that contain NAs will yield a matrix containing NAs in the corresponding cells. Multi- plying two matrices will result in a matrix containing NAs in the appropriate rows and columns. • All matrix algebra functions and commands will generate NAs, since these operations are undefined. For example, the Cholesky factorization of a matrix that contains NAs will contain NAs.
- 283. 276—Chapter 11. Matrix Language • All utility functions and commands will work as before, with NAs treated like any other value. Copying the contents of a vector into a matrix using colplace (p. 662) will place the contents, including NAs, into the target matrix. • All of the matrix element functions will propagate NAs when appropriate. Taking the absolute value of a matrix will yield a matrix containing absolute values for non-miss- ing cells and NAs for cells that contain NAs. • The descriptive statistics functions are based upon the non-missing subset of the ele- ments in the matrix. You can always find out how many values were used in the com- putations by using the @obs or the @nas functions. Matrix Views and Procs The individual object descriptions in the Object Reference list the various views and procs for the various matrix objects. Listings are available for matrices (“Matrix,” on page 378), vec- tors (“Vector,” on page 869), symmetric matrices (“Sym,” on page 699), rowvectors (“Rowvector,” on page 501), and coefs (“Coef” on page 16). Matrix Graph and Statistics Views All of the matrix objects, with the exception of the scalar object, have windows and views. For example, you may display line and bar graphs for each column of the matrix Z: z.line z.bar(p) Each column will be plotted against the row number of the matrix. Additionally, you can compute descriptive statistics for each column of a matrix, as well as the correlation and covariance matrix between the columns of the matrix: z.stats z.cor z.cov By default, EViews performs listwise deletion by column when computing correlations and covariances, so that each group of column statistics is computed using the largest possible set of observations. The full syntax for the commands to display and print these and other views is provided in the reference for the specific object (e.g., matrix, sym) in the Object Reference. Matrix Input and Output EViews provides you with the ability to read and write files directly from matrix objects using the read and write procedures. 10 5
- 284. Matrix Operations versus Loop Operations—277 You must supply the name of the source file. If you do not include the optional path specifi- cation, EViews will look for the file in the default directory. The input specification follows the source file name. Path specifications may point to local or network drives. If the path specification contains a space, you must enclose the entire expression in double quotes “”. In reading from a file, EViews first fills the matrix with NAs, places the first data element in the “(1,1)” element of the matrix, then continues to read the data by row or by column, depending upon the options set. The following command reads data into MAT1 from an Excel file CPS88 in the network drive specified in the path directory. The data are read by column, and the upper left data cell is A2. mat1.read(a2,s=sheet3) "net1dr 1cps88.xls" To read the same file by row, you should use the “t” option: mat1.read(a2,t,s=sheet3) "net1dr 1cps88.xls" To write data from a matrix, use the write keyword, enter the desired options, then the name of the output file. For example: mat1.write mydt.txt writes the data in MAT1 into the ASCII file “Mydt.TXT” located in the default directory. There are many more options for controlling reading and writing of matrix data; Chapter 5. “Basic Data Handling,” on page 119 of User’s Guide I offers extensive discussion. See also the descriptions for the matrix procs Matrix::read and Matrix::write (similar descrip- tions are available for the other matrix objects.) Matrix Operations versus Loop Operations You may perform matrix operations using element operations and loops instead of the built- in functions and commands. For example, the inner product of two vectors may be com- puted by evaluating the vectors element-by-element: scalar inprod1 = 0 for !i = 1 to @rows(vec1) inprod1 = inprod1 + vec1(!i)*vec2(!i) next This approach will, however, generally be much slower than using the built-in function: scalar inprod2 = @inner(vec1, vec2) You should use the built-in matrix operators rather than loop operators whenever you can. The matrix operators are always much faster than the equivalent loop operations.
- 285. 278—Chapter 11. Matrix Language Similarly, suppose, for example, that you wish to subtract the column mean from each ele- ment of a matrix. Such a calculation might be useful in constructing a fixed effects regres- sion estimator. First, consider a slow method involving only loops and element operations: matrix x = @convert(mygrp1) scalar xsum for !i = 1 to @columns(x) xsum = 0 for !j = 1 to @rows(x) xsum = xsum+x(!j,!i) next xsum = xsum/@rows(x) for !j = 1 to @rows(x) x(!j,!i) = x(!j,!i)-xsum next next The loops are used to compute a mean for each column of data in X, and then to subtract the value of the mean from each element of the column. A faster method for subtracting col- umn means uses the built-in operators and functions: matrix x = @convert(mygrp1) vector xmean = @cmeans(x) x = x - @scale(@ones(@rows(x), @columns(x)),@transpose(xmean)) The first line converts the data in MYGRP1 into the matrix X. The second line computes the column means of X and saves the results in XMEAN. The last line subtracts the matrix of column means from X. Note that we first create a temporary matrix of ones, then use the @scale function to scale each column using the element in the corresponding column of the transpose of XMEAN. Summary of Automatic Resizing of Matrix Objects When you perform a matrix object assignment, EViews will resize, where possible, the des- tination object to accommodate the contents of the source matrix. This resizing will occur if the destination object type can be modified and sized appropriately and if the values of the destination may be assigned without ambiguity. You can, for example, assign a matrix to a vector and vice versa, you can assign a scalar to a matrix, but you cannot assign a matrix to a scalar since EViews does not permit scalar resizing. The following table summarizes the rules for resizing of matrix objects as a result of declara- tions of the form object_type y = x
- 286. Summary of Automatic Resizing of Matrix Objects—279 where object_type is an EViews object type, or is the result of an assignment statement for Y after an initial declaration, as in: object_type y y = x Each row of the table corresponds to the specified type of the destination object, Y. Each col- umn represents the type and size of the source object, X. Each cell of the table shows the type and size of object that results from the declaration or assignment. Object type and size for source X Object type for Y coef(p) matrix(p,q) coef(k) coef(p) invalid matrix(n,k) matrix(p,1) matrix(p,q) rowvector(k) rowvector(p) invalid scalar invalid invalid sym(k) invalid sym(p) if p=q vector(n) vector(p) invalid Object type and size for source X Object type for Y rowvector(q) scalar coef(k) coef(q) coef(k) matrix(n,k) matrix(1,q) matrix(n,k) rowvector(k) rowvector(q) rowvector(k) scalar invalid scalar sym(k) invalid invalid vector(n) rowvector(q) vector(n) Object type and size for source X Object type for Y sym(p) vector(p) coef(k) invalid coef(p) matrix(n,k) matrix(p,p) matrix(p,1) rowvector(k) invalid vector(p) scalar invalid invalid sym(k) sym(p) invalid vector(n) invalid vector(p)
- 287. 280—Chapter 11. Matrix Language For example, consider the command matrix(500,4) y = x where X is a coef of size 50. The object type is given by examining the table entry corre- sponding to row “matrix Y” ( ), and column “coef X” ( ). The entry reads “matrix( )”, so that the result Y is a matrix. Similarly, the command: vector(30) y = x where X is a 10 element rowvector, yields the 10 element rowvector Y. In essence, EViews first creates the 30 element rowvector Y, then resizes it to match the size of X, then finally assigns the values of X to the corresponding elements of Y. n 500 k, 4 p 50 p 1, 50 1
- 288. Chapter 12. Command Reference Commands The following list summarizes the EViews basic commands. Commands for working with matrix objects are listed in Chapter 18. “Matrix Language Ref- erence,” on page 653, and EViews programming expressions are described in Chapter 19. “Programming Language Reference,” beginning on page 701. A list of views and procedures available for each EViews object may be found in Chapter 1. “Object View and Procedure Reference,” on page 2 of the Object Reference. Command Actions do.........................execute action without opening window (p. 349). freeze....................create view object (p. 362). print .....................print view (p. 454). show.....................show object window (p. 473). Global Commands cd .........................change default directory (p. 305). exit .......................exit the EViews program (p. 352). output...................redirect printer output (p. 428). param ...................set parameter values (p. 453). rndseed.................set the seed of the random number generator (p. 463). smpl .....................set the sample range (p. 476). Object Creation Commands alpha ....................alpha series. coef.......................coefficient vector. data ......................enter data from keyboard (p. 339). equation................equation object. factor ....................factor analysis object. frml ......................numeric or alpha series object with a formula for auto-updating (p. 363). genr......................numeric or alpha series object (p. 365). graph ....................graph object—create using a graph command or by merging exist- ing graphs. group....................group object. link.......................series or alpha link object.
- 289. 282—Chapter 12. Command Reference logl ...................... likelihood object. matrix .................. matrix object. model................... model object. pool ..................... pool object. rowvector ............. rowvector object. sample ................. sample object. scalar ................... scalar object. series ................... numeric series. spool.................... spool object. sspace .................. sspace object. string ................... string object. svector ................. svector object. sym...................... sym object. system.................. system object. table..................... table object. text ...................... text object. userobj................. user object. valmap................. valmap object. var ....................... var estimation object. vector................... vector object. Object Container, Data, and File Commands ccopy ................... copy series from DRI database (p. 305). cfetch ................... fetch series from DRI database (p. 308). clabel ................... display DRI series description (p. 310). close .................... close object, program, or workfile (p. 310). copy..................... copy objects within and between workfiles, workfile pages, and databases (p. 327). db ........................ open or create a database (p. 340). dbcopy ................. make copy of a database (p. 340). dbcreate ............... create a new database (p. 342). dbdelete ............... delete a database (p. 344). dbopen................. open a database (p. 344). dbpack ................. pack a database (p. 346). dbrebuild.............. rebuild a database (p. 347). dbrename ............. rename a database (p. 347). delete ................... delete objects from a workfile (p. 348). driconvert............. convert the entire DRI database to an EViews database (p. 349). expand ................. expand workfile range (p. 352).
- 290. Commands—283 fetch .....................fetch objects from databases or databank files (p. 355). hconvert ...............convert an entire Haver Analytics database to an EViews database (p. 378). hfetch ...................fetch series from a Haver Analytics database (p. 379). hlabel ...................obtain label from a Haver Analytics database (p. 380). import...................imports data from a foreign file or a previously saved workfile into the current default workfile (p. 382). importattr .............imports observation values stored inside one or more series in a second workfile page into the attribute fields of objects within the current workfile page (p. 390). importmat.............imports data from a foreign file into a matrix object in the current workfile (p. 392) importtbl...............imports data from a foreign file into a table object in the current workfile (p. 398). load ......................load a workfile (p. 406). logclear.................clear the log window corresponding to the program (p. 406). logmode................set the log settings of specified message types (p. 407). logmsg..................add a line of text to the program log (p. 409). logsave..................save the program log to a text file (p. 409). open .....................open a program or text (ASCII) file (p. 418). optsave .................save the current EViews global options settings .INI files into a directory (p. 424). optset....................replace the current EViews global options settings .INI files with ones based in a different directory (p. 425). pageappend...........append observations to workfile page (p. 430). pagecontract..........contract workfile page (p. 431). pagecopy...............copy contents of a workfile page (p. 432). pagecreate.............create a workfile page (p. 434). pagedelete.............delete a workfile page (p. 440). pageload ...............load one or more pages into a workfile from a workfile or a foreign data source (p. 440). pagerefresh ...........refresh all links and auto-series in the active workfile page—primar- ily used to refresh links that use external database data (p. 441). pagerename...........rename a workfile page (p. 442). pagesave ...............save page into a workfile or a foreign data source (p. 442). pageselect .............make specified page active (p. 444). pagestack..............reshape the workfile page by stacking observations (p. 445). pagestruct .............apply a workfile structure to the page (p. 448).
- 291. 284—Chapter 12. Command Reference pageunlink ........... break links in all link objects and auto-updating series (formulae) in the active workfile page (p. 450). pageunstack ......... reshape the workfile page by unstacking observations into multiple series (p. 451). range.................... reset the workfile range (p. 458). read ..................... import data from a foreign disk file into series (p. 458). save ..................... save workfile to disk (p. 468). sort ...................... sort the workfile (p. 479). store..................... store objects in database and databank files (p. 484). unlink.................. break links and auto-updating series (formulae) in the specified series objects (p. 501). wfclose................. close the active workfile (p. 507). wfcompare ........... compare the contents of the current workfile or page with the con- tents of a different workfile, page, or database (p. 510). wfcreate ............... create a new workfile (p. 510). wfdetails .............. change the details displayed in the current workfile window (p. 514). wfopen................. open workfile or foreign source data as a workfile (p. 515). wfrefresh.............. refresh all links and auto-series in the active workfile—primarily used to refresh links that use external database data (p. 528). wfsave.................. save workfile to disk as a workfile or a foreign data source (p. 529). wfselect................ change active workfile page (p. 532). wfstats ................. display the workfile statistics and summary view (p. 533). wfunlink .............. break links in all link objects and auto-updating series (formulae) in the active workfile (p. 533). wfuse ................... activate a workfile (p. 534). workfile ............... create or change active workfile (p. 535). write .................... write series to a disk file (p. 535). Object Utility Commands close .................... close window of an object, program, or workfile (p. 310). copy..................... copy objects (p. 327). delete ................... delete objects (p. 348). rename................. rename object (p. 461). Object Assignment Commands data...................... enter data from keyboard (p. 339). frml...................... assign formula for auto-updating to a numeric or alpha series object (p. 363). genr ..................... create numeric or alpha series object (p. 365).
- 292. Commands—285 rndint ...................assign random integer values to object (p. 462). rndseed.................set random number generator seed (p. 463). Graph Creation Commands Graph creation is discussed in detail in “Graph Creation Command Summary” on page 895 of the Object Reference. All of the page numbers in this section refer to the Object Reference. area ......................area graph (p. 897). band .....................area band graph (p. 900). bar........................bar graph (p. 902). boxplot .................boxplot graph (p. 907). distplot .................distribution graph (p. 909). dot........................dot plot graph (p. 916). errbar ...................error bar graph (p. 920). hilo.......................high-low(-open-close) graph (p. 922). line.......................line-symbol graph (p. 924). mixed ...................mixed type graph (p. 927). pie ........................pie chart (p. 930). qqplot ...................quantile-quantile graph (p. 932). scat.......................scatterplot (p. 936). scatmat .................matrix of all pairwise scatterplots (p. 941). scatpair.................scatterplot pairs graph (p. 944). seasplot.................seasonal line graph (p. 948). spike.....................spike graph (p. 949). xyarea...................XY area graph (p. 953). xybar ....................XY bar graph (p. 956). xyline ...................XY line graph (p. 958). xypair ...................XY pairs graph (p. 961). Table Commands setcell ...................format and fill in a table cell (p. 469). setcolwidth ...........set width of a table column (p. 471). setline...................place a horizontal line in table (p. 472). tabplace ................insert a table into another table (p. 489). Note that with the exception of tabplace, these commands are supported primarily for backward compatibility. There is a more extensive set of table procs for working with and customizing tables. See “Table Procs,” on page 770 of the Object Reference. Programming Commands addin ....................register an Add-in (p. 288).
- 293. 286—Chapter 12. Command Reference adduo................... register a user object (p. 290). commandcap........ send text to the command capture window (p. 327). exec ..................... execute a program (p. 350). logclear ................ clears the log window of a program (p. 406). logmode ............... sets logging of specified messages (p. 407). logmsg ................. adds a line of text to the program log (p. 409). logsave................. saves the program log to a text file (p. 409). open..................... open a program file (p. 418). optimize............... find the solution to a user-defined optimization problem (p. 419). output .................. redirects print output to objects or files (p. 428). poff ...................... turns off automatic printing in programs (p. 718). pon ...................... turns on automatic printing in programs (p. 718). program ............... create a new program (p. 455). run....................... run a program (p. 467). spawn .................. spawn a new process (p. 480). statusline.............. send message to the status line (p. 482). tic ........................ reset the timer (p. 494). toc ....................... display elapsed time (since timer reset) in seconds (p. 495). External Interface Commands xclose................... close an open connection to an external application (p. 537). xget...................... retrieve data from an external application into an EViews object (p. 537). xlog...................... switch on or off the external application log inside EViews (p. 540). xopen................... open a connection to an external application (p. 540). xput ..................... send an EViews object to an external application (p. 542). xrun..................... run a command in an external application (p. 544). Interactive Use Commands The following commands have object command forms (e.g., Equation::arch). These commands are particularly suited for interactive command line use. In general, we recom- mend that you use the object forms of the commands. arch ..................... estimate autoregressive conditional heteroskedasticity (ARCH and GARCH) models (p. 291). archtest ................ LM test for the presence of ARCH in the residuals (p. 295). ardl ...................... least squares with autoregressive distributed lags (p. 296). auto ..................... Breusch-Godfrey serial correlation Lagrange Multiplier (LM) test (p. 298). binary .................. binary dependent variable models (includes probit, logit, gompit) models (p. 299).
- 294. Commands—287 breakls.................. least squares with breakpoints and breakpoint determination (p. 370). cause ....................pairwise Granger causality tests (p. 304). censored ...............estimate censored and truncated regression (includes tobit) models (p. 306). chow.....................Chow breakpoint and forecast tests for structural change (p. 309). coint .....................cointegration test (p. 311). cointreg.................estimate cointegrating equation using FMOLS, CCR, or DOLS (p. 319). cor........................correlation matrix (p. 334). count ....................count data modeling (includes poisson, negative binomial and quasi-maximum likelihood count models) (p. 335). cov .......................covariance matrix (p. 337). cross .....................cross correlogram (p. 338). data ......................enter data from keyboard (p. 339). facbreak................factor breakpoint test for stability (p. 352). factest ...................estimate a factor analysis model (p. 353). fit .........................static forecast from an equation (p. 358). forecast .................dynamic forecast from an equation (p. 360). glm.......................estimate a Generalized Linear Model (GLM) (p. 365). gmm.....................estimate an equation using generalized method of moments (p. 370). heckit ...................estimate a selection equation using the Heckman ML or 2-step method (p. 376). hist .......................histogram and descriptive statistics (p. 380). hpf........................Hodrick-Prescott filter (p. 381). liml.......................estimate an equation using Limited Information Maximum Likeli- hood and K-class Estimation (p. 404). logit......................logit (binary) estimation (p. 407). ls ..........................equation using least squares or nonlinear least squares (p. 410). midas....................estimate an equation using Mixed Data Sampling (MIDAS) regres- sion (p. 416). ordered .................ordinal dependent variable models (includes ordered probit, ordered logit, and ordered extreme value models) (p. 426). probit....................probit (binary) estimation (p. 455). qreg ......................estimate an equation using quantile regression (p. 455). reset......................Ramsey’s RESET test for functional form (p. 461). robustls.................robust regression (M-estimation, S-estimation and MM-estimation) (p. 464).
- 295. 288—Chapter 12. Command Reference seas...................... seasonal adjustment for quarterly and monthly time series (p. 469). smooth................. exponential smoothing (p. 474). solve .................... solve a model (p. 478). stats ..................... descriptive statistics (p. 481). stepls ................... estimate an equation using stepwise regression (p. 483). switchreg ............. exogenous and Markov switching regression (p. 486). testadd ................. likelihood ratio test for adding variables to equation (p. 490). testdrop................ likelihood ratio test for dropping variables from equation (p. 491). threshold.............. threshold least squares, including threshold autoregression (p. 491). tsls....................... estimate an equation using two-stage least squares regression (p. 496). ubreak.................. Andrews-Quandt test for unknown breakpoint (p. 500). uroot.................... unit root test (p. 502). varest ................... specify and estimate a VAR or VEC (p. 506). Command Entries The following section provides an alphabetical listing of commands. Each entry outlines the command syntax and associated options, and provides examples and cross references. Register a program file as an EViews Add-in. Syntax addin(options) [path]prog_name registers the specified program file as an EViews Add-in. Note that the program file should have a “.PRG” extension, which you need not specify in the prog_name. If you do not provide the optional path specification, EViews looks for the program file in the default EViews Add-ins directory. Explicit path specifications containing “.” and “..” (to indicate the current level and one directory level up) are evaluated relative the directory of the installer program in which the addin command is specified, or the EViews default directory if addin is run from the com- mand line. You may use the special “<addins>”directory keyword in your path specification. addin Programming Commands
- 296. addin—289 Options Examples addin(proc="myaddin", desc="This is my add-in", version="1.0") .myaddin.prg registers the file “Myaddin.prg” as a global Add-in, with the user-defined global command myaddin, no menu support, and no assigned documentation file. The description “This is my add-in” will appear in the main Add-ins management dialog. The version number is “1.0”. Note that the “.” indicates the directory from which the program containing the addin command was run, or the EViews default directory if addin is run interactively. type=arg Specify the Add-ins type, where arg is the name of a EViews object type. The default is to create a global Add- in. Specifying an object-specific Add-in using a matrix object as in “type=matrix”, “type=vector”, etc. will register the Add-in for all matrix object types (including coef, rowvec- tor, and sym objects). Sample objects do not support object-specific Add-ins so that “type=sample” is not allowed. proc=arg User--defined command/procedure name. If omitted, the Add-in will not have a command form. menu=arg Text for the Add-in menu entry. If omitted, the Add-in will not have an associated menu item. Note that you may use the “&” symbol in the entry text to indicate that the following character should be used as a menu shortcut. desc=arg Brief description of the Add-in that will be displayed in the Add-ins management dialog. docs=arg Path and filename for the Add-in documentation. Determi- nation of the path follows the rules specified above for the addin filename. version=arg Version number of the Add-in. If no version number is sup- plied, EViews will assume version 1.0. url=arg Specify the location of an XML file containing information on the Add-in used for updating the Add-in to the latest version. If not supplied, EViews will default to an XML file hosted on the EViews website. nowarn Removes the prompt warning that an add-in already exists with the same name (and forces an overwrite of that add- in).
- 297. 290—Chapter 12. Command Reference addin(type="graph", menu="Add US Recession Shading", proc="recshade", docs=".recession shade.txt", desc="Applies US recession shading to a graph object.") .recshade.prg registers the file “Recshade.prg” as a graph specific Add-in. The Add-in supports the object- command recshade, has an object-specific menu item “Add US Recession Shading”, and has a documentation file “Recession shade.txt”. addin(type=equation, menu="Simple rolling regression", proc=roll, docs="<addins>RollRoll.pdf", desc="Rolling Regression - simple version", url="www.mysite.com/myaddins.xml", version="1.2") "<addins>Rollroll.prg" registers the Add-in file “Roll.prg” as an equation specific Add-in. Note that the documenta- tion and program files are located in the “Roll” subdirectory of the default Add-ins directory. The XML file located at www.mysite.com/myaddins.xml is used when checking for avail- able updates for the Add-in, and the current version number is set to “1.2”. Cross-references See Chapter 8. “Add-ins,” on page 187 for a detailed discussion of Add-ins. Register an EViews user object class. Syntax adduo(options) [path]definition_name registers the specified definition file as an EViews user object. Note that the definition file should have a “.INI” extension. If you do not provide the optional path specification, EViews looks for the program file in the default EViews user objects directory. Explicit path specifications containing “.” and “..” (to indicate the current level and one directory level up) are evaluated relative the directory of the installer program in which the adduo command is specified, or the EViews default directory if adduo is run from the com- mand line. adduo Programming Commands
- 298. arch—291 Options Examples adduo(name="roll", desc="Rolling Regression Object") .rolldef.ini registers the roll class of user object, specifying a description of “Rolling Regression Object”, and using the definition file rolldef.ini, located in the same folder as the installer program. adduo(name="resstore", version="1.0", url="www.mysite.com/myuos.xml") .resstoredef.ini registers the resstore class of user object, specifying the version number as “1.0”, and using the XML file located at “www.mysite.com/myuos.xml” to check for updates. Cross-references See Chapter 9. “User Objects,” on page 213 for a discussion of user objects. Estimate generalized autoregressive conditional heteroskedasticity (GARCH) models. Syntax arch(p,q,options) y [x1 x2 x3] [@ p1 p2 [@ t1 t2]] arch(p,q,options) y=expression [@ p1 p2 [@ t1 t2]] The ARCH command estimates a model with p ARCH terms and q GARCH terms. Note the order of the arguments in which the ARCH and GARCH terms are entered, which gives prece- dence to the ARCH term. name=arg Specify the name of the user object class. desc=arg Brief description of the user object that will be displayed in the user object management dialog. docs=arg Path and filename for the user object documentation. Determination of the path follows the rules specified above for the adduo filename. version=arg Version number of the Add-in. If no version number is sup- plied, EViews will assume version 1.0. url=arg Specify the location of an XML file containing information on the Add-in used for updating the Add-in to the latest version. If not supplied, EViews will default to an XML file hosted on the EViews website. arch Interactive Use Commands
- 299. 292—Chapter 12. Command Reference The maximum value for or is 9; values above will be set to 9. The minimum value for is 1. The minimum value for is 0. If either or is not specified, EViews will assume a corresponding order of 1. Thus, a GARCH(1, 1) is assumed by default. After the “ARCH” keyword, specify the dependent variable followed by a list of regressors in the mean equation. By default, no exogenous variables (except for the intercept) are included in the conditional variance equation. If you wish to include variance regressors, list them after the mean equa- tion using an “@”-sign to separate the mean from the variance equation. When estimating component ARCH models, you may specify exogenous variance regressors for the permanent and transitory components. After the mean equation regressors, first list the regressors for the permanent component, followed by an “@”-sign, then the regressors for the transitory component. A constant term is always included as a permanent compo- nent regressor. Options General Options egarch Exponential GARCH. parch[=arg] Power ARCH. If the optional arg is provided, the power parameter will be set to that value, otherwise the power parameter will be estimated. cgarch Component (permanent and transitory) ARCH. asy=integer (default=1) Number of asymmetric terms in the Power ARCH or EGARCH model. The maximum number of terms allowed is 9. thrsh=integer (default=0) Number of threshold terms for GARCH and Component models. The maximum number of terms allowed is 9. For Component models, “thrsh” must take a value of 0 or 1. archm=arg ARCH-M (ARCH in mean) specification with the condi- tional standard deviation (“archm=sd”), the conditional variance (“archm=var”), or the log of the conditional vari- ance (“archm= log”) entered as a regressor in the mean equation. tdist [=number] Estimate the model assuming that the residuals follow a conditional Student’s t-distribution (the default is the con- ditional normal distribution). Providing the optional num- ber greater than two will fix the degrees of freedom to that value. If the argument is not provided, the degrees of free- dom will be estimated. p q p q p q
- 300. arch—293 ged [=number] Estimate the model assuming that the residuals follow a conditional GED (the default is the conditional normal dis- tribution). Providing a positive value for the optional argu- ment will fix the GED parameter. If the argument is not provided, the parameter will be estimated. z Turn of backcasting for both initial MA innovations and ini- tial variances. backcast=n Backcast weight to calculate value used as the presample conditional variance. Weight needs to be greater than 0 and less than or equal to 1; the default value is 0.7. Note that a weight of 1 is equivalent to no backcasting, i.e. using the unconditional residual variance as the presample condi- tional variance. optmethod = arg Optimization method: “bfgs” (BFGS); “newton” (Newton- Raphson), “opg” or “bhhh” (OPG or BHHH), “legacy” (EViews legacy). “bfgs” is the default for new equations. optstep = arg Step method: “marquardt” (Marquardt - default); “dogleg” (Dogleg); “linesearch” (Line search). (Applicable when “optmethod=bfgs”, “optmethod=new- ton” or “optmethod=opg”.) b Use Berndt-Hall-Hall-Hausman (BHHH) as maximization algorithm. The default is Marquardt. (Applicable when “optmethod=legacy”.) cov=arg Covariance method: “ordinary” (default method based on inverse of the estimated information matrix), “huber” or “white” (Huber-White sandwich method), “bollerslev” (Bollerslev-Wooldridge method). covinfo = arg Information matrix method: “opg” (OPG); “hessian” (observed Hessian), “ (Applicable when non-legacy “optmethod=” with “cov=ordinary”.) h Bollerslev-Wooldridge robust quasi-maximum likelihood (QML) covariance/standard errors. (Applicable for “optmethod=legacy” when estimating assuming normal errors.) m=integer Set maximum number of iterations. c=scalar Set convergence criterion. The criterion is based upon the maximum of the percentage changes in the scaled coeffi- cients.
- 301. 294—Chapter 12. Command Reference GARCH options Saved results Most of the results saved for the ls command are also available after ARCH estimation; see ls (p. 410) for details. Examples arch(4, 0, m=1000, cov=bollerslev) sp500 c estimates an ARCH(4) model with a mean equation consisting of the series SP500 regressed on a constant. The procedure will perform up to 1000 iterations, and will report Bollerslev- Wooldridge robust QML standard errors upon completion. The commands: c = 0.1 arch(thrsh=1, s, mean=var) @pch(nys) c ar(1) s Use the current coefficient values in “C” as starting values (see also param (p. 453)). s=number Specify a number between zero and one to determine start- ing values as a fraction of preliminary LS estimates (out of range values are set to “s=1”). numericderiv / -numericderiv [Do / do not] use numeric derivatives only. If omitted, EViews will follow the global default. fastderiv / -fastderiv [Do / do not] use fast derivative computation. If omitted, EViews will follow the global default. Available only for legacy estimation (“optmeth=legacy”). showopts / -showopts [Do / do not] display the starting coefficient values and estimation options in the estimation output. coef=arg Specify the name of the coefficient vector (if specified by list); the default behavior is to use the “C” coefficient vec- tor. prompt Force the dialog to appear from within a program. p Print estimation results. vt Variance target of the constant term. (Can’t be used with integrated specifications). integrated Restrict GARCH model to be integrated, i.e. IGARCH. (Can’t be used with variance targeting).
- 302. archtest—295 estimate a TARCH(1, 1)-in-mean specification with the mean equation relating the percent change of NYS to a constant, an AR term of order 1, and a conditional variance (GARCH) term. The first line sets the default coefficient vector to 0.1, and the “s” option uses these values as coefficient starting values. The command: arch(1, 2, asy=0, parch=1.5, ged=1.2) dlog(ibm)=c(1)+c(2)* dlog(sp500) @ r estimates a symmetric Power ARCH(2, 1) (autoregressive GARCH of order 2, and moving average ARCH of order 1) model with GED errors. The power of model is fixed at 1.5 and the GED parameter is fixed at 1.2. The mean equation consists of the first log difference of IBM regressed on a constant and the first log difference of SP500. The conditional variance equa- tion includes an exogenous regressor R. Cross-references See Chapter 25. “ARCH and GARCH Estimation,” on page 231 of User’s Guide II for a discus- sion of ARCH models. See Equation::garch (p. 92) in the Object Reference for the equivalent object command. See also Equation::garch (p. 92) and Equation::makegarch (p. 123) in the Object Ref- erence. Test for autoregressive conditional heteroskedasticity (ARCH). Carries out Lagrange Multiplier (LM) tests for ARCH in the residuals. Note that a more general version of the ARCH test is available using Equation::archtest (p. 44) as described in the Object Reference. Syntax archtest(options) Options You must specify the order of ARCH for which you wish to test. The number of lags to be included in the test equation should be provided in parentheses after the arch keyword. Other Options: archtest Interactive Use Commands prompt Force the dialog to appear from within a program. p Print output from the test.
- 303. 296—Chapter 12. Command Reference Examples ls output c labor capital archtest(4) Regresses OUTPUT on a constant, LABOR, and CAPITAL, and tests for ARCH up to order 4. equation eq1.arch sp500 c archtest(4) Estimates a GARCH(1,1) model with mean equation of SP500 on a constant and tests for additional ARCH up to order 4. Note that when performing an archtest after an arch esti- mation, EViews uses the standardized residuals (the residual of the mean equation divided by the estimated conditional standard deviation) to form the test. Cross-references See “ARCH LM Test” on page 186 of User’s Guide II for further discussion of testing ARCH and Chapter 25. “ARCH and GARCH Estimation,” on page 231 of User’s Guide II for a discus- sion of working with ARCH models in EViews. See Equation::archtest (p. 44) in the Object Reference for the equivalent object com- mand. See Equation::hettest (p. 106) in the Object Reference for a more general version of the ARCH test. Estimate an equation with autoregressive distributed lags using least squares. Syntax ardl(options) dynamic_eqn @ static_regs The dynamic equation should be the dependent variable followed by a list of dynamic regressors (regressors with lags). The static regressors should be a list of static regressors, not including a constant or trend term. ardl Interactive Use Commands
- 304. ardl—297 Options Examples wfopen http://www.stern.nyu.edu/~wgreene/Text/Edition7/TableF5- 2.txt ardl(deplags=8, reglags=8) log(realcons) log(realgdp) @ @expand(@quarter, @droplast) This example uses data from Greene (2008, page 685), containing quarterly US macroeco- nomic variables between 1950 and 2000. The first line of this example downloads the data set, the second line creates an equation object and estimates an ARDL model with the log of real consumption as the dependent variable, and the log of real GDP as a dynamic regressor. fixed Do not use automatic selection for lag lengths. This option must be used with the deplags= and reglags= options. deplags=int Set the number of lags for the dependent variable to int. If automatic selection is used, this sets the maximum number of possible lags. If fixed lags are used (the fixed option is set), this fixes the number of lags. reglags=int Set the number of lags for the explanatory variables (dynamic regressors) to int. If automatic selection is used, this sets the maximum number of possible lags. If fixed lags are used (the fixed option is set), this fixes the number of lags for each regressor. trend=key Set the trend specification. key may take values of “const” (include a constant, default), “none” (do not include a trend or constant), or “linear” (include both a constant and a linear trend). ic=key Set the method of automatic model selection. key may take values of “aic” (Akaike information criterion, default), “bic” (Schwarz criterion), “hq” (Hannan-Quinn criterion) or “rbar2” (Adjusted R-squared, not applicable in panel workfiles). nodf Do not perform degree of freedom corrections in computing coefficient covariance matrix. The default is to use degree of freedom corrections. coef=arg Specify the name of the coefficient vector (if specified by list); the default behavior is to use the “C” coefficient vec- tor. prompt Force the dialog to appear from within a program. p Print results.
- 305. 298—Chapter 12. Command Reference Quarterly dummy variables are included as static regressors. Automatic model selection is used to determine the number of lags of log(realcons) and log(realgdp). ardl(deplags=3, reglags=3, fixed) log(realcons) log(realgdp) @ @expand(@quarter, @droplast) This command estimates a second model, replicating Example 20.4 from Greene, estimating a model fixed at 3 lags of the dependent variable and 3 lags of the regressor. wfopen oecd.wf1 ardl(fixed, deplags=1, reglags=1) log(cons) log(inf) log(inc) This example estimates a panel ARDL model using the workfile OECD.wf1. This model rep- licates that given in the original Pesaran, Shin and Smith 1999 paper. Model selection is not used to choose the optimal lag lengths, rather a fixed single lag of both the dependent vari- able and the regressor are used. Cross-references See “Autoregressive Distributed Lag (ARDL) Models,” beginning on page 283 of User’s Guide II for further discussion. Compute serial correlation LM (Lagrange multiplier) test. Carries out Breusch-Godfrey Lagrange Multiplier (LM) tests for serial correlation in the esti- mation residuals from the default equation. Syntax auto(order, options) You must specify the order of serial correlation for which you wish to test. You should spec- ify the number of lags in parentheses after the auto keyword, followed by any additional options. Options Examples To regress OUTPUT on a constant, LABOR, and CAPITAL, and test for serial correlation of up to order four you may use the commands: ls output c labor capital auto Interactive Use Commands prompt Force the dialog to appear from within a program. p Print output from the test.
- 306. binary—299 auto(4) The commands: output(t) c:resultartest.txt equation eq1.ls cons c y y(-1) auto(12, p) perform a regression of CONS on a constant, Y and lagged Y, and test for serial correlation of up to order twelve. The first line redirects printed tables/text to the “Artest.TXT” file. Cross-references See “Serial Correlation LM Test” on page 96 of User’s Guide II for further discussion of the Breusch-Godfrey test. See Equation::auto (p. 48) in the Object Reference for the corresponding equation view. Estimate binary dependent variable models. Estimates models where the binary dependent variable Y is either zero or one (probit, logit, gompit). Syntax binary(options) y x1 [x2 x3 ...] binary(options) specification Options binary Interactive Use Commands d=arg (default=“n”) Specify likelihood: normal likelihood function, probit (“n”), logistic likelihood function, logit (“l”), Type I extreme value likelihood function, Gompit (“x”). optmethod = arg Optimization method: “bfgs” (BFGS); “newton” (Newton- Raphson), “opg” or “bhhh” (OPG or BHHH), “legacy” (EViews legacy). Newton-Raphson is the default method. optstep = arg Step method: “marquardt” (Marquardt); “dogleg” (Dog- leg); “linesearch” (Line search). Marquardt is the default method. cov=arg Covariance method: “ordinary” (default method based on inverse of the estimated information matrix), “huber” or “white” (Huber-White sandwich method), “glm” (GLM method).
- 307. 300—Chapter 12. Command Reference Examples To estimate a logit model of Y using a constant, WAGE, EDU, and KIDS, and computing Huber-White standard errors, you may use the command: binary(d=l,cov=huber) y c wage edu kids Note that this estimation uses the default global optimization options. The commands: param c(1) .1 c(2) .1 c(3) .1 binary(s) y c x2 x3 estimate a probit model of Y on a constant, X2, and X3, using the specified starting values. The commands: coef beta_probit = @coefs matrix cov_probit = @coefcov covinfo = arg Information matrix method: “opg” (OPG); “hessian” (observed Hessian - default). (Applicable when non-legacy “optmethod=”.) h Huber-White quasi-maximum likelihood (QML) standard errors and covariances. (Legacy option applicable when “optmethod=legacy”). g GLM standard errors and covariances. (Legacy option applicable when “optmethod=legacy”). m=integer Set maximum number of iterations. c=scalar Set convergence criterion. The criterion is based upon the maximum of the percentage changes in the scaled coeffi- cients. The criterion will be set to the nearest value between 1e-24 and 0.2. s Use the current coefficient values in “C” as starting values (see also param (p. 453)). s=number Specify a number between zero and one to determine start- ing values as a fraction of EViews default values (out of range values are set to “s=1”). showopts / -showopts [Do / do not] display the starting coefficient values and estimation options in the estimation output. coef=arg Specify the name of the coefficient vector (if specified by list); the default behavior is to use the “C” coefficient vec- tor. prompt Force the dialog to appear from within a program. p Print results.
- 308. breakls—301 store the estimated coefficients and coefficient covariances in the coefficient vector BETA_PROBIT and matrix COV_PROBIT. Cross-references See “Binary Dependent Variable Models” on page 315 of User’s Guide II for additional dis- cussion. See Equation::binary (p. 49) in the Object Reference for the corresponding equation method. Estimation by linear least squares regression with breakpoints. Syntax breakls(options) y z1 [z2 z3 ...] [@nv x1 x2 x3 ...] List the dependent variable first, followed by a list of the independent variables that have coefficients which are allowed to vary across breaks, followed optionally by the keyword @nv and a list of non-varying coefficient variables. Options Breakpoint Options breakls Interactive Use Commands method=arg (default=“seqplus1”) Breakpoint selection method: “seqplus1” (sequential tests of single versus breaks), “seqall” (sequential test of all possible versus breaks), “glob” (tests of global vs. no breaks), “globplus1” (tests of versus globally determined breaks), “globinfo” (information criteria evaluation), “user” (user-specified break dates). select=arg Sub-method setting (options depend on “method=”). (1) if “method=glob”: Sequential (“seq”) (default), Highest significant (“high”), (“udmax”), (“wdmax”). (2) if “method=globinfo”: Schwarz criterion (“bic” or “sic”) (default), Liu-Wu-Zidek criterion (“lwz”). trim=arg (default=5) Trimming percentage for determining minimum segment size (5, 10, 15, 20, 25). maxbreaks=integer (default=5) Maximum number of breakpoints to allow (not applica- ble if “method=seqall”). maxlevels=integer (default=5) Maximum number of break levels to consider in sequen- tial testing (applicable when “method=sequall”). l 1 l l 1 l l l 1 l UDmax WDmax
- 309. 302—Chapter 12. Command Reference General Options breaks="arg" User-specified break dates entered in double quotes. For use when “method=user”. size=arg (default=5) Test sizes for use in sequential determination and final test evaluation (10, 5, 2.5, 1) corresponding to 0.10, 0.05, 0.025, 0.01, respectively heterr Assume regimes specific error distributions in variance computation. commondata Assume a common distribution for the data across seg- ments (only applicable if original equation is estimated with a robust covariance method, “heterr” is not speci- fied). w=arg Weight series or expression. wtype=arg (default=“istdev”) Weight specification type: inverse standard deviation (“ist- dev”), inverse variance (“ivar”), standard deviation (“stdev”), variance (“var”). wscale=arg Weight scaling: EViews default (“eviews”), average (“avg”), none (“none”). The default setting depends upon the weight type: “eviews” if “wtype=istdev”, “avg” for all others. cov=keyword Covariance type (optional): “white” (White diagonal matrix), “hac” (Newey-West HAC). nodf Do not perform degree of freedom corrections in computing coefficient covariance matrix. The default is to use degree of freedom corrections. covlag=arg (default=1) Whitening lag specification: integer (user-specified lag value), “a” (automatic selection). covinfosel=arg (default=“aic”) Information criterion for automatic selection: “aic” (Akaike), “sic” (Schwarz), “hqc” (Hannan-Quinn) (if “lag=a”). covmaxlag=integer Maximum lag-length for automatic selection (optional) (if “lag=a”). The default is an observation-based maximum of .T 1 3
- 310. breakls—303 Examples breakls m1 c unemp uses the Bai-Perron sequential versus tests to determine the optimal breaks in a model regressing M1 on the breaking variables C and UNEMP. breakls(method=glob, select=high) m1 c unemp uses the global Bai-Perron versus none tests to determine the breaks. The selected break will be the highest significant number of breaks. breakls(size=5, trim=10) m1 c unemp lowers the sequential test size from 0.10 to 0.05, and raises the trimming to 10 percent. breakls(method=user, break=”1990q1 2010q4”) m1 c @nv unemp estimates the model with two user-specified break dates. In addition, the variable UNEMP is restricted to have common coefficients across the regimes. Cross-reference See Chapter 5. “Least Squares with Breakpoints,” beginning on page 425 of User’s Guide II for discussion. See also “Multiple Breakpoint Tests” on page 198 of User’s Guide II. covkern=arg (default=“bart”) Kernel shape: “none” (no kernel), “bart” (Bartlett, default), “bohman” (Bohman), “daniell” (Daniel), “parzen” (Parzen), “parzriesz” (Parzen-Riesz), “parzgeo” (Parzen- Geometric), “parzcauchy” (Parzen-Cauchy), “quadspec” (Quadratic Spectral), “trunc” (Truncated), “thamm” (Tukey-Hamming), “thann” (Tukey-Hanning), “tparz” (Tukey-Parzen). covbw=arg (default=“fixednw” ) Kernel Bandwidth: “fixednw” (Newey-West fixed), “andrews” (Andrews automatic), “neweywest” (Newey- West automatic), number (User-specified bandwidth). covnwlag=integer Newey-West lag-selection parameter for use in nonpara- metric kernel bandwidth selection (if “covbw=newey- west”). covbwoffset=inte- ger (default=0) Apply integer offset to bandwidth chosen by automatic selection method (“bw=andrews” or “bw=neweywest”). covbwint Use integer portion of bandwidth chosen by automatic selection method (“bw=andrews” or “bw=neweywest”). coef=arg Specify the name of the coefficient vector; the default behavior is to use the “C” coefficient vector. prompt Force the dialog to appear from within a program. p Print basic estimation results. L 1 L L
- 311. 304—Chapter 12. Command Reference See Equation::multibreak (p. 133) of Object Reference for estimation of regression equa- tions with breaks. Granger causality test. Performs pairwise Granger causality tests between (all possible) pairs of the listed series or group of series. Syntax cause(n, options) ser1 ser2 ser3 Following the keyword, list the series or group of series for which you wish to test for Granger causality. Options You must specify the number of lags n to use for the test by providing an integer in paren- theses after the keyword. Note that the regressors of the test equation are a constant and the specified lags of the pair of series under test. Other options: Examples To compute Granger causality tests of whether GDP Granger causes M1 and whether M1 Granger causes GDP, you may enter the command: cause(4) gdp m1 The regressors of each test are a constant and four lags of GDP and M1. cause(12,p) m1 gdp r prints the result of six pairwise Granger causality tests for the three series. The regressors of each test are a constant and twelve lags of the two series under test (and do not include lagged values of the third series). Cross-references See “Granger Causality” on page 566 of User’s Guide I for a discussion of Granger’s approach to testing hypotheses about causality. See also Group::cause (p. 288) in the Object Reference for the corresponding group view. cause Interactive Use Commands prompt Force tcointehe dialog to appear from within a program. p Print output of the test.
- 312. cd—305 Copy one or more series from the DRI Basic Economics database to EViews data bank (.DB) files. You must have the DRI database installed on your computer to use this feature. Syntax ccopy series_name Type the name of the series or wildcard expression for series you want to copy after the ccopy keyword. The data bank files will be stored in the default directory with the same name as the series names in the DRI database. You can supply path information to indicate the directory for the data bank files. Examples The command: ccopy lhur copies the DRI series LHUR to “Lhur.DB” file in the default path directory. ccopy b:gdp c:nipadatagnet copies the GDP series to the “Gdp.DB” file in the “B:” drive and the GNET series to the “Gnet.DB” file in “c:nipadata”. Cross-references See also cfetch (p. 308), clabel (p. 310), store (p. 484), fetch (p. 355). Change default directory. The cd command changes the current default working directory. The current working direc- tory is displayed in the “Path=...” message in the bottom right of the EViews window. Note that the default directory is not used for processing of include files (see include (p. 713)). Syntax cd path_name ccopy Object Container, Data, and File Commands cd Global Commands
- 313. 306—Chapter 12. Command Reference Examples To change the default directory to “sample data” in the “a:” drive, you may issue the com- mand: cd "a:sample data" Notice that the directory name is surrounded by double quotes. If your name does not con- tain spaces, you may omit the quotes. The command: cd a:test changes the default directory to “a:test”. cd “<myonedrive>:test changes the default directory to the cloud location, MYONEDRIVE. Subsequent save operations will save into the default directory, unless you specify a differ- ent directory at the time of the operation. Cross-references See Chapter 3. “Workfile Basics,” on page 41 of User’s Guide I for further discussion of basic operations in EViews. See also “include” on page 713, wfsave (p. 529), pagesave (p. 442), and save (p. 468). Estimation of censored and truncated models. Estimates models where the dependent variable is either censored or truncated. The allow- able specifications include the standard Tobit model. Syntax censored(options) y x1 [x2 x3] censored(options) specification Options censored Interactive Use Commands l=number (default=0) Set value for the left censoring limit. r=number (default=none) Set value for the right censoring limit. l=series_name, i Set series name of the indicator variable for the left censor- ing limit.
- 314. censored—307 r=series_name, i Set series name of the indicator variable for the right cen- soring limit. t Estimate truncated model. d=arg (default=“n”) Specify error distribution: normal (“n”), logistic (“l”), Type I extreme value (“x”). optmethod = arg Optimization method: “bfgs” (BFGS); “newton” (Newton- Raphson), “opg” or “bhhh” (OPG or BHHH), “legacy” (EViews legacy). Newton-Raphson is the default method. optstep = arg Step method: “marquardt” (Marquardt); “dogleg” (Dog- leg); “linesearch” (Line search). Marquardt is the default method. cov=arg Covariance method: “ordinary” (default method based on inverse of the estimated information matrix), “huber” or “white” (Huber-White sandwich method). covinfo = arg Information matrix method: “opg” (OPG); “hessian” (observed Hessian - default). (Applicable when non-legacy “optmethod=”). h Huber-White quasi-maximum likelihood (QML) standard errors and covariances. (Legacy option Applicable when “optmethod=legacy”). m=integer Set maximum number of iterations. c=scalar Set convergence criterion. The criterion is based upon the maximum of the percentage changes in the scaled coeffi- cients. The criterion will be set to the nearest value between 1e-24 and 0.2. s Use the current coefficient values in “C” as starting values (see also param (p. 453)). s=number Specify a number between zero and one to determine start- ing values as a fraction of EViews default values (out of range values are set to “s=1”). showopts / -showopts [Do / do not] display the starting coefficient values and estimation options in the estimation output. coef=arg Specify the name of the coefficient vector (if specified by list); the default behavior is to use the “C” coefficient vec- tor. prompt Force the dialog to appear from within a program. p Print results.
- 315. 308—Chapter 12. Command Reference Examples The command: censored(cov=huber) hours c wage edu kids estimates a censored regression model of HOURS on a constant, WAGE, EDU, and KIDS with QML standard errors. This command uses the default normal likelihood, with left-censoring at HOURS=0, no right censoring, and the quadratic hill climbing algorithm. Cross-references See Chapter 2. “Discrete and Limited Dependent Variable Models,” on page 315 of User’s Guide II for discussion of censored and truncated regression models. See Equation::censored (p. 59) in the Object Reference for the corresponding equation method. Fetch a series from the DRI Basic Economics database into a workfile. cfetch reads one or more series from the DRI Basic Economics Database into the active workfile. You must have the DRI database installed on your computer to use this feature. Syntax cfetch series_name Examples cfetch lhur gdp gnet reads the DRI series LHUR, GDP, and GNET into the current active workfile, performing fre- quency conversions if necessary. Cross-references EViews’ automatic frequency conversion is described in “Frequency Conversion,” beginning on page 160 of User’s Guide I. See also ccopy (p. 305), clabel (p. 310), store (p. 484), fetch (p. 355). Change default directory. See cd (p. 305). cfetch Object Container, Data, and File Commands chdir Global Commands
- 316. chow—309 Chow test for stability. Carries out Chow breakpoint or Chow forecast tests for parameter constancy. Syntax chow(options) obs1 [obs2 obs3 ...] @ x1 x2 x3 You must provide the breakpoint observations (using dates or observation numbers) to be tested. To specify more than one breakpoint, separate the breakpoints by a space. For the Chow breakpoint test, if the equation is specified by list and contains no linear terms, you may specify a subset of the regressors to be tested for a breakpoint after an “@” sign. Options Examples The commands: ls m1 c gdp cpi ar(1) chow 1970Q1 1980Q1 perform a regression of M1 on a constant, GDP, and CPI with first order autoregressive errors, and employ a Chow breakpoint test to determine whether the parameters before the 1970’s, during the 1970’s, and after the 1970’s are “stable”. To regress the log of SPOT on a constant, the log of P_US, and the log of P_UK, and to carry out the Chow forecast test starting from 1973, enter the commands: ls log(spot) c log(p_us) log(p_uk) chow(f) 1973 To test whether only the constant term and the coefficient on the log of P_US prior to and after 1970 are “stable” enter the commands: chow 1970 @ c log(p_us) Cross-references See “Chow's Breakpoint Test” on page 194 of User’s Guide II for further discussion. chow Interactive Use Commands f Chow forecast test. For this option, you must specify a sin- gle breakpoint to test (default performs breakpoint test). p Print the result of test.
- 317. 310—Chapter 12. Command Reference See also ubreak (p. 500). See Equation::facbreak (p. 86), Equation::breaktest (p. 56), Equation::ubreak (p. 173), and Equation::rls (p. 153) in the Object Reference for related equation object views. Display a DRI Basic Economics database series description. clabel reads the description of a series from the DRI Basic Economics Database and dis- plays it in the status line at the bottom of the EViews window. Use this command to verify the contents of a given DRI database series name. You must have the DRI database installed on your computer to use this feature. Syntax clabel series_name Examples clabel lhur displays the description of the DRI series LHUR on the status line. Cross-references See also ccopy (p. 305), cfetch (p. 308), read (p. 458), fetch (p. 355). Close object, program, or workfile. Closing an object eliminates its window. If the object is named, it is still displayed in the workfile as an icon, otherwise it is deleted. Closing a program or workfile eliminates its win- dow and removes it from memory. If a workfile has changed since you activated it, you will see a dialog box asking if you want to save it to disk. Syntax close option_or_name Options option_or_name may be either an object name or one of the following options: clabel Object Container, Data, and File Commands close Object Container, Data, and File Commands
- 318. coint—311 Examples close gdp graph1 table2 closes the three objects GDP, GRAPH1, and TABLE2. lwage.hist close lwage opens the LWAGE window and displays the histogram view of LWAGE, then closes the win- dow. close @all closes all windows within EViews. close @objects closes all objects in EViews, leaving workfiles, programs, and database windows open. Cross-references See Chapter 1. “Introduction,” on page 5 of User’s Guide I for a discussion of basic control of EViews. See also exit (p. 352) and save (p. 468). Perform either (1) Johansen’s system cointegration test, (2) Engle-Granger or Phillips- Ouliaris single equation cointegration testing, or (3) Pedroni, Kao, or Fisher panel cointe- gration testing for the specified series. Syntax There are three forms for the coint command which depend on the form of the test you wish to perform: @all Close down everything. This is the same as clicking on Close All from the EViews main menu. @objects Close down all objects. This is the same as clicking on Close All Objects from the EViews main menu. @wf Close down all open workfiles. @db Close down all open databases. @prg Close down all open program files. coint Interactive Use Commands
- 319. 312—Chapter 12. Command Reference Johansen Cointegration Test Syntax coint(test_option, n, option) ser1 ser2 [...ser3 ser4 ...] [@ x1 x2 x3 ...] uses the coint keyword followed by the test_option and the number of lags n, and if desired, an “@”-sign followed by a list of exogenous variables. The first option must be one of the following six test options: Options for the Johansen Test a No deterministic trend in the data, and no intercept or trend in the cointegrating equation. b No deterministic trend in the data, and an intercept but no trend in the cointegrating equation. c Linear trend in the data, and an intercept but no trend in the cointegrating equation. d Linear trend in the data, and both an intercept and a trend in the cointegrating equation. e Quadratic trend in the data, and both an intercept and a trend in the cointegrating equation. s Summarize the results of all 5 options (a-e). restrict Impose restrictions as specified by the append (coint) proc. m = integer Maximum number of iterations for restricted estimation (only valid if you choose the restrict option). c = scalar Convergence criterion for restricted estimation. (only valid if you choose the restrict option). save = mat_name Stores test statistics as a named matrix object. The save= option stores a matrix, where is the num- ber of endogenous variables in the VAR. The first column contains the eigenvalues, the second column contains the maximum eigenvalue statistics, the third column contains the trace statistics, and the fourth column contains the log likelihood values. The i-th row of columns 2 and 3 are the test statistics for rank . The last row is filled with NAs, except the last column which contains the log likeli- hood value of the unrestricted (full rank) model. k 1 4 k i 1–
- 320. coint—313 This type of cointegration testing may be used in a non-panel workfile. For Fisher combined testing using the Johansen framework, see below. The remaining options for the Johansen cointegration test are outlined below (“Options for the Johansen Test” on page 312). Note that the output for cointegration tests displays p-values for the rank test statistics. These p-values are computed using the response surface coefficients as estimated in MacK- innon, Haug, and Michelis (1999). The 0.05 critical values are also based on the response surface coefficients from MacKinnon-Haug-Michelis. Note: the reported critical values assume no exogenous variables other than an intercept and trend. Single Equation Test Syntax coint(method=arg, options) ser1 ser2 [...ser3 ser4 ...] [@determ determ_spec] [@reg- determ regdeterm_spec] where Cointegrating equation specifications that include a constant, linear, or quadratic trends, should use the “trend=” option to specify those terms. If any of those terms are in the sto- chastic regressors equations but not in the cointegrating equation, they should be specified using the “regtrend=” option. Deterministic trend regressors that are not covered by the list above may be specified using the keywords @determ and @regdeterm. To specify deterministic trend regressors that enter into the regressor and cointegrating equations, you should add the keyword @determ fol- lowed by the list of trend regressors. To specify deterministic trends that enter in the regres- cvtype=ol Display 0.05 and 0.01 critical values from Osterwald- Lenum (1992). This option reproduces the output from version 4. The default is to display critical values based on the response surface coefficients from MacKinnon-Haug-Michelis (1999). Note that the argument on the right side of the equals sign are letters, not numbers 0-1). cvsize=arg (default=0.05) Specify the size of MacKinnon-Haug-Michelis (1999) criti- cal values to be displayed. The size must be between 0.0001 and 0.9999; values outside this range will be reset to the default value of 0.05. This option is ignored if you set “cvtype=ol”. prompt Force the dialog to appear from within a program. p Print results. method=arg Test method: Engle-Granger residual test (“eg”), Phillips- Ouliaris residual test (“po”).
- 321. 314—Chapter 12. Command Reference sor equations but not the cointegrating equation, you should include the keyword @regdeterm followed by the list of trend regressors. Note that the p-values for the test statistics are based on simulations, and do not account for any user-specified deterministic regressors. This type of cointegration testing may be used in a non-panel workfile. The remaining options for the single equation cointegration tests are outlined below. Options for Single Equation Tests Options for the Engle-Granger Test The following options determine the specification of the Engle-Granger test (Augmented Dickey-Fuller) equation and the calculation of the variances used in the test statistic. trend=arg (default=“const”) Specification for the powers of trend to include in the cointegrating equation: None (“none”), Constant (“const”), Linear trend (“linear”), Quadratic trend (“quadratic”). Note that the specification implies all trends up to the specified order so that choosing a quadratic trend instructs EViews to include a constant and a linear trend term along with the quadratic. regtrend=arg (default=“none”) Additional trends to include in the regressor equations (but not the cointegrating equation): None (“none”), Constant (“const”), Linear trend (“linear”), Quadratic trend (“qua- dratic”). Only trend orders higher than those specified by “trend=” will be considered. Note that the specification implies all trends up to the specified order so that choosing a quadratic trend instructs EViews to include a constant and a linear trend term along with the quadratic. lag=arg (default=“a”) Method of selecting the lag length (number of first differ- ence terms) to be included in the regression: “a” (auto- matic information criterion based selection), or integer (user-specified lag length). lagtype=arg (default=“sic”) Information criterion or method to use when computing automatic lag length selection: “aic” (Akaike), “sic” (Schwarz), “hqc” (Hannan-Quinn), “msaic” (Modified Akaike), “msic” (Modified Schwarz), “mhqc” (Modified Hannan-Quinn), “tstat” (t-statistic).
- 322. coint—315 Options for the Phillips-Ouliaris Test The following options control the computation of the symmetric and one-sided long-run variances in the Phillips-Ouliaris test. Basic Options: HAC Whitening Options: maxlag=integer Maximum lag length to consider when performing auto- matic lag-length selection default= where is the number of coefficients in the cointegrat- ing equation. Applicable when “lag=a”. lagpval=number (default=0.10) Probability threshold to use when performing automatic lag-length selection using a t-test criterion. Applicable when both “lag=a” and “lagtype=tstat”. nodf Do not degree-of-freedom correct estimates of the vari- ances. prompt Force the dialog to appear from within a program. p Print results. trend=arg (default=“const”) Specification for the powers of trend to include in the cointegrating equation: None (“none”), Constant (“const”), Linear trend (“linear”), Quadratic trend (“quadratic”). Note that the specification implies all trends up to the specified order so that choosing a quadratic trend instructs EViews to include a constant and a linear trend term along with the quadratic. regtrend=arg (default=“none”) Additional trends to include in the regressor equations (but not the cointegrating equation): None (“none”), Constant (“const”), Linear trend (“linear”), Quadratic trend (“qua- dratic”). Only trend orders higher than those specified by “trend=” will be considered. Note that the specification implies all trends up to the specified order so that choosing a quadratic trend instructs EViews to include a constant and a linear trend term along with the quadratic. nodf Do not degree-of-freedom correct the coefficient covariance estimate. prompt Force the dialog to appear from within a program. p Print results. int min T k– 3 12,( ) T 100 1 4 ( ) k
- 323. 316—Chapter 12. Command Reference HAC Kernel Options: Panel Syntax coint(option) ser1 ser2 [...ser3 ser4 ...] The coint command tests for cointegration among the series in the group. The second form of the command should be used with panel structured workfiles. Options for the Panel Tests For panel cointegration tests, you may specify the type using one of the following keywords: Depending on the type selected above, the following may be used to indicate deterministic trends: lag=arg (default=0) Lag specification: integer (user-specified lag value), “a” (automatic selection). infosel=arg (default=“aic”) Information criterion for automatic selection: “aic” (Akaike), “sic” (Schwarz), “hqc” (Hannan-Quinn) (if “lag=a”). maxlag=integer Maximum lag-length for automatic selection (optional) (if “lag=a”). The default is an observation-based maximum. kern=arg (default=“bart”) Kernel shape: “none” (no kernel), “bart” (Bartlett, default), “bohman” (Bohman), “daniell” (Daniel), “parzen” (Parzen), “parzriesz” (Parzen-Riesz), “parzgeo” (Parzen- Geometric), “parzcauchy” (Parzen-Cauchy), “quadspec” (Quadratic Spectral), “trunc” (Truncated), “thamm” (Tukey-Hamming), “thann” (Tukey-Hanning), “tparz” (Tukey-Parzen). bw=arg (default=“nwfixed”) Bandwidth: “fixednw” (Newey-West fixed), “andrews” (Andrews automatic), “neweywest” (Newey-West auto- matic), number (User-specified bandwidth). nwlag=integer Newey-West lag-selection parameter for use in nonpara- metric bandwidth selection (if “bw=neweywest”). bwint Use integer portion of bandwidth. Pedroni (default) Pedroni (1994 and 2004). Kao Kao (1999) Fisher Fisher - pooled Johansen
- 324. coint—317 Additional Options: const (default) Include a constant in the test equation. Applicable to Pedroni and Kao tests. trend Include a constant and a linear time trend in the test equa- tion. Applicable to Pedroni tests. none Do not include a constant or time trend. Applicable to Pedroni tests. a, b, c, d, or e Indicate deterministic trends using the “a”, “b”, “c”, “d”, and “e” keywords, as detailed above in “Options for the Johansen Test” on page 312. Applicable to Fisher tests. ac=arg (default=“bt”) Method of estimating the frequency zero spectrum: “bt” (Bartlett kernel), “pr” (Parzen kernel), “qs” (Quadratic Spectral kernel). Applicable to Pedroni and Kao tests. band=arg (default=“nw”) Method of selecting the bandwidth, where arg may be “nw” (Newey-West automatic variable bandwidth selec- tion), or a number indicating a user-specified common bandwidth. Applicable to Pedroni and Kao tests. lag=arg For Pedroni and Kao tests, the method of selecting lag length (number of first difference terms) to be included in the residual regression. For Fisher tests, a pair of numbers indicating lag. infosel=arg (default=“sic”) Information criterion to use when computing automatic lag length selection: “aic” (Akaike), “sic” (Schwarz), “hqc” (Hannan-Quinn). Applicable to Pedroni and Kao tests. maxlag=int Maximum lag length to consider when performing auto- matic lag length selection, where int is an integer. The default is where is the length of the cross-section. Applicable to Pedroni and Kao tests. disp=arg (default=500) Maximum number of individual results to be displayed. int min Ti 3 12, Ti 100 1 4 ( ) Ti
- 325. 318—Chapter 12. Command Reference Examples Johansen test coint(s,4) x y z summarizes the results of the Johansen cointegration test for the series X, Y, and Z for all five specifications of trend. The test equation uses lags of up to order four. Engle-Granger Test coint(method=eg) x y z performs the default Engle-Granger test on the residuals from a cointegrating equation which includes a constant. The number of lags is determined using the SIC criterion and an observation-based maximum number of lags. coint(method=eg, trend=linear, lag=a, lagtype=tstat, lagpval=.15, maxlag=10) x y z employs a cointegrating equation that includes a constant and linear trend, and uses a sequential t-test starting at lag 10 with threshold probability 0.15 to determine the number of lags. coint(method=eg, lag=5) x y z conducts an Engle-Granger cointegration test on the residuals from a cointegrating equation with a constant, using a fixed lag of 5. Phillips-Ouliaris Test coint(method=po) x y z performs the default Phillips-Ouliaris test on the residuals from a cointegrating equation with a constant, using a Bartlett kernel and Newey-West fixed bandwidth. coint(method=po, bw=andrews, kernel=quadspec, nodf) x y z estimates the long-run covariances using a Quadratic Spectral kernel, Andrews automatic bandwidth, and no degrees-of-freedom correction. coint(method=po, trend=linear, lag=1, bw=4) x y z estimates a cointegrating equation with a constant and linear trend, and performs the Phil- lips-Ouliaris test on the residuals by computing the long-run covariances using AR(1) pre- whitening, a fixed bandwidth of 4, and the Bartlett kernel. Panel Tests For a panel structured workfile, coint(pedroni,maxlag=3,infosel=sic) x y z prompt Force the dialog to appear from within a program. p Print results.
- 326. cointreg—319 performs Pedroni’s residual-based panel cointegration test with automatic lag selection with a maximum lag limit of 3. Automatic selection based on Schwarz criterion. Cross-references See Chapter 20. “Cointegration Testing,” on page 961 of User’s Guide II for details on the var- ious cointegration tests. See Equation::coint (p. 64) and Group::coint (p. 290) in the Object Reference for the related object routines. Estimate a cointegrating equation using Fully Modified OLS (FMOLS), Canonical Cointe- grating Regression (CCR), or Dynamic OLS (DOLS) in single time series settings, and Panel FMOLS and DOLS in panel workfiles. Syntax cointreg(options) y x1 [x2 x3 ...] [@determ determ_spec] [@regdeterm regdeter- m_spec] List the coint keyword, followed by the dependent variable and a list of the cointegrating variables. Cointegrating equation specifications that include a constant, linear, or quadratic trends, should use the “trend=” option to specify those terms. If any of those terms are in the sto- chastic regressors equations but not in the cointegrating equation, they should be specified using the “regtrend=” option. Deterministic trend regressors that are not covered by the list above may be specified using the keywords @determ and @regdeterm. To specify deterministic trend regressors that enter into the regressor and cointegrating equations, you should add the keyword @determ fol- lowed by the list of trend regressors. To specify deterministic trends that enter in the regres- sor equations but not the cointegrating equation, you should include the keyword @regdeterm followed by the list of trend regressors. cointreg Interactive Use Commands
- 327. 320—Chapter 12. Command Reference Basic Options In addition to these options, there are specialized options for each estimation method. Panel Options method=arg (default=“fmols”) Estimation method: Fully Modified OLS (“fmols”), Canoni- cal Cointegrating Regression (“ccr”), Dynamic OLS (“dols”) Note that CCR estimation is not available in panel set- tings. trend=arg (default=“const”) Specification for the powers of trend to include in the cointegrating and regressor equations: None (“none”), Constant (“const”), Linear trend (“linear”), Quadratic trend (“quadratic”). Note that the specification implies all trends up to the specified order so that choosing a quadratic trend instructs EViews to include a constant and a linear trend term along with the quadratic. regtrend=arg (default=“none”) Additional trends to include in the regressor equations (but not the cointegrating equation): None (“none”), Constant (“const”), Linear trend (“linear”), Quadratic trend (“qua- dratic”). Only trend orders higher than those specified by “trend=” will be considered. Note that the specification implies all trends up to the specified order so that choosing a quadratic trend instructs EViews to include a constant and a linear trend term along with the quadratic. regdiff Estimate the regressor equation innovations directly using the difference specifications. coef=arg Specify the name of the coefficient vector; the default behavior is to use the “C” coefficient vector. prompt Force the dialog to appear from within a program. p Print results. panmethod=arg (default=“pooled”) Panel estimation method: pooled (“pooled”), pooled weighted (“weighted”), grouped (“grouped”) pancov=sandwich Estimate the coefficient covariance using a sandwich method that allows for cross-section heterogeneity.
- 328. cointreg—321 Options for FMOLS and CCR To estimate FMOLS or CCR use the “method=fmols” or “method=ccr” options. The follow- ing options control the computation of the symmetric and one-sided long-run covariance matrices and the estimate of the coefficient covariance. HAC Whitening Options HAC Kernel Options Coefficient Covariance lag=arg (default=0) Lag specification: integer (user-specified lag value), “a” (automatic selection). infosel=arg (default=“aic”) Information criterion for automatic selection: “aic” (Akaike), “sic” (Schwarz), “hqc” (Hannan-Quinn) (if “lag=a”). maxlag=integer Maximum lag-length for automatic selection (optional) (if “lag=a”). The default is an observation-based maximum. kern=arg (default=“bart”) Kernel shape: “none” (no kernel), “bart” (Bartlett, default), “bohman” (Bohman), “daniell” (Daniell), “parzen” (Parzen), “parzriesz” (Parzen-Riesz), “parzgeo” (Parzen- Geometric), “parzcauchy” (Parzen-Cauchy), “quadspec” (Quadratic Spectral), “trunc” (Truncated), “thamm” (Tukey-Hamming), “thann” (Tukey-Hanning), “tparz” (Tukey-Parzen). bw=arg (default=“nwfixed”) Bandwidth:: “fixednw” (Newey-West fixed), “andrews” (Andrews automatic), “neweywest” (Newey-West auto- matic), number (User-specified bandwidth). nwlag=integer Newey-West lag-selection parameter for use in nonpara- metric bandwidth selection (if “bw=neweywest”). bwoffset=integer (default=0) Apply integer offset to bandwidth chosen by automatic selection method (“bw=andrews” or “bw=neweywest”). bwint Use integer portion of bandwidth chosen by automatic selection method (“bw=andrews” or “bw=neweywest”). nodf Do not degree-of-freedom correct the coefficient covariance estimate.
- 329. 322—Chapter 12. Command Reference Panel Options Options for DOLS To estimate using DOLS use the “method=dols” option. The following options control the specification of the lags and leads and the estimate of the coefficient covariance. For the default covariance calculation or “cov=hac”, the following options control the com- putation of the long-run variance or robust covariance: HAC Covariance Whitening Options (if default covariance or “cov=hac”) hetfirst Estimate the first-stage regression assuming heterogeneous coefficients. For FMOLS specifications estimated using pooled or pooled weighted methods (“panmethod =pooled”, “panmethod=weighted”) lltype=arg (default=“fixed”) Lag-lead method: fixed values (“fixed”), automatic selec- tion - Akaike (“aic”), automatic - Schwarz (“sic”), auto- matic - Hannan-Quinn (“hqc”), None (“none”). lag=arg Lag specification: integer (required user-specified number of lags if “lltype=fixed”). lead=arg Lead specification: integer (required user-specified number of lags if “lltype=fixed”). maxll=integer Maximum lag and lead-length for automatic selection (optional user-specified integer if “lltype=” is used to specify automatic selection). The default is an observation- based maximum. cov=arg Coefficient covariance method: (default) long-run variance scaled OLS, unscaled OLS (“ols”), White (“white”), Newey-West (“hac”). nodf Do not degree-of-freedom correct the coefficient covariance estimate. covlag=arg (default=0) Lag specification: integer (user-specified lag value), “a” (automatic selection). covinfosel=arg (default=“aic”) Information criterion for automatic selection: “aic” (Akaike), “sic” (Schwarz), “hqc” (Hannan-Quinn) (if “lag=a”). covmaxlag=integer Maximum lag-length for automatic selection (optional) (if “lag=a”). The default is an observation-based maximum.
- 330. cointreg—323 HAC Covariance Kernel Options (if default covariance or “cov=hac”) Panel Options Weighted coefficient or coefficient covariance estimation in panel DOLS requires individual estimates of the long-run variances of the residuals. You may compute these estimates using the standard default long-run variance options, or you may choose to estimate it using the ordinary variance. For weighted estimation we have: covkern=arg (default=“bart”) Kernel shape: “none” (no kernel), “bart” (Bartlett, default), “bohman” (Bohman), “daniell” (Daniel), “parzen” (Parzen), “parzriesz” (Parzen-Riesz), “parzgeo” (Parzen- Geometric), “parzcauchy” (Parzen-Cauchy), “quadspec” (Quadratic Spectral), “trunc” (Truncated), “thamm” (Tukey-Hamming), “thann” (Tukey-Hanning), “tparz” (Tukey-Parzen). covbw=arg (default=“nwfixed”) Bandwidth: “fixednw” (Newey-West fixed), “andrews” (Andrews automatic), “neweywest” (Newey-West auto- matic), number (User-specified bandwidth). covnwlag=integer Newey-West lag-selection parameter for use in nonpara- metric bandwidth selection (if “covbw=neweywest”). covbwoffset=integer (default=0) Apply integer offset to bandwidth chosen by automatic selection method (“bw=andrews” or “bw=neweywest”). covbwint Use integer portion of bandwidth chosen by automatic selection method (“bw=andrews” or “bw=neweywest”). panwgtlag=arg (default=0) Lag specification: integer (user-specified lag value), “a” (automatic selection). panwgtinfosel=arg (default=“aic”) Information criterion for automatic selection: “aic” (Akaike), “sic” (Schwarz), “hqc” (Hannan-Quinn) (if “lrvarlag=a”). panwgtmaxlag=inte- ger Maximum lag-length for automatic selection (optional) (if “lrvarlag=a”). The default is an observation-based maxi- mum. panwgtkern=arg (default=“bart”) Kernel shape: “none” (no kernel), “bart” (Bartlett, default), “bohman” (Bohman), “daniell” (Daniell), “parzen” (Parzen), “parzriesz” (Parzen-Riesz), “parzgeo” (Parzen- Geometric), “parzcauchy” (Parzen-Cauchy), “quadspec” (Quadratic Spectral), “trunc” (Truncated), “thamm” (Tukey-Hamming), “thann” (Tukey-Hanning), “tparz” (Tukey-Parzen).
- 331. 324—Chapter 12. Command Reference For the coefficient covariance estimation we have: panwgtbw=arg (default=“nwfixed”) Bandwidth:: “fixednw” (Newey-West fixed), “andrews” (Andrews automatic), “neweywest” (Newey-West auto- matic), number (User-specified bandwidth). panwgtnwlag=integer Newey-West lag-selection parameter for use in nonpara- metric bandwidth selection (if “bw=neweywest”). panwgtbwoff- set=integer (default=0) Apply offset to automatically selected bandwidth. For settings where “cov=hac”, “covkern=” is specified, and “covbw=” is not user-specified. panwgtbwint Use integer portion of bandwidth chosen by automatic selection method (“bw=andrews” or “bw=neweywest”). lrvar=ordinary Compute DOLS estimates of the long-run residual variance used in covariance calculation using the ordinary variance. lrvarlag=arg (default=0) For DOLS estimates of the long-run residual variance used in covariance calculation, lag specification: integer (user- specified lag value), “a” (automatic selection). lrvarinfosel=arg (default=“aic”) For DOLS estimates of the long-run residual variance used in covariance calculation, information criterion for auto- matic selection: “aic” (Akaike), “sic” (Schwarz), “hqc” (Hannan-Quinn) (if “lrvarlag=a”). lrvarmaxlag=integer For DOLS estimates of the long-run residual variance used in covariance calculation, maximum lag-length for auto- matic selection (optional) (if “lrvarlag=a”). The default is an observation-based maximum. lrvarkern=arg (default=“bart”) For DOLS estimates of the long-run residual variance used in covariance calculation, Kernel shape: “none” (no ker- nel), “bart” (Bartlett, default), “bohman” (Bohman), “dan- iell” (Daniell), “parzen” (Parzen), “parzriesz” (Parzen- Riesz), “parzgeo” (Parzen-Geometric), “parzcauchy” (Parzen-Cauchy), “quadspec” (Quadratic Spectral), “trunc” (Truncated), “thamm” (Tukey-Hamming), “thann” (Tukey- Hanning), “tparz” (Tukey-Parzen). lrvarbw=arg (default=“nwfixed”) For DOLS estimates of the long-run residual variance used in covariance calculation, bandwidth:: “fixednw” (Newey- West fixed), “andrews” (Andrews automatic), “neweywest” (Newey-West automatic), number (User-specified band- width).
- 332. cointreg—325 Examples FMOLS and CCR To estimate, by FMOLS, the cointegrating equation for LC and LY including a constant, you may use: cointreg(nodf, bw=andrews) lc ly The long-run covariances are estimated nonparametrically using a Bartlett kernel and a bandwidth determined by the Andrews automatic selection method. The coefficient covari- ances are estimated with no degree-of-freedom correction. To include a linear trend term in a model where the long-run covariances computed using the Quadratic Spectral kernel and a fixed bandwidth of 10, enter: cointreg(trend=linear, nodf, bw=10, kern=quadspec) lc ly A model with a cubic trend may be estimated using: cointreg(trend=linear, lags=2, bw=neweywest, nwlag=10, kernel=parzen) lc ly @determ @trend^3 Here, the long-run covariances are estimated using a VAR(2) prewhitened Parzen kernel with Newey-West nonparametric bandwidth determined using 10 lags in the autocovariance calculations. cointreg(trend=quadratic, bw=andrews, lags=a, infosel=aic, kernel=none, regdiff) lc ly @regdeterm @trend^3 estimates a restricted model with a cubic trend term that does not appear in the cointegrat- ing equation using a parametric VARHAC with automatic lag length selection based on the AIC. The residuals for the regressors equations are obtained by estimating the difference specification. To estimate by CCR, we provide the “method=ccr” option. The command lrvarnwlag=integer For DOLS estimates of the long-run residual variance used in covariance calculation, Newey-West lag-selection param- eter for use in nonparametric bandwidth selection (if “bw=neweywest”). lrvarbwoffset=inte- ger (default=0) For DOLS estimates of the long-run residual variance used in covariance calculation, apply offset to automatically selected bandwidth. For settings where “cov=hac”, “covkern=” is specified, and “covbw=” is not user-specified. lrvarbwint For DOLS estimates of the long-run residual variance used in covariance calculation, use integer portion of band- width.
- 333. 326—Chapter 12. Command Reference cointreg(method=ccr, lag=2, bw=andrews, kern=quadspec) lc ly estimates, by CCR, the constant only model using a VAR(2) prewhitened Quadratic Spectral and Andrews automatic bandwidth selection. cointreg(method=ccr, trend=linear, lag=a, maxlag=5, bw=andrews, kern=quadspec) lc ly modifies the previous estimates by adding a linear trend term to the cointegrating and regressors equations, and changing the VAR prewhitening to automatic selection using the default SIC with a maximum lag length of 5. cointreg(method=ccr, trend=linear, regtrend=quadratic, lag=a, maxlag=5, bw=andrews) lc ly adds a quadratic trend term to the regressors equations only, and changes the kernel to the default Bartlett. DOLS cointreg(method=dols, trend=linear, nodf, lag=4, lead=4) lc ly estimates the linear specification using DOLS with four lags and leads. The coefficient cova- riance is obtained by rescaling the no d.f.-correction OLS covariance using the long-run vari- ance of the residuals computed using the default Bartlett kernel and default fixed Newey- West bandwidth. cointreg(method=dols, trend=quadratic, nodf, lag=4, lead=2, covkern=bohman, covbw=10) lc ly @determ @trend^3 estimates a cubic specification using 4 lags and 2 leads, where the coefficient covariance uses a Bohman kernel and fixed bandwidth of 10. cointreg(method=dols, trend=quadratic, nodf, lag=4, lead=2, cov=hac, covkern=bohman, covbw=10) lc ly @determ @trend^3 estimates the same specification using a HAC covariance in place of the scaled OLS covari- ance. cointreg(method=dols, trend=quadratic, lltype=none, cov=ols) lc ly @determ @trend^3 computes the Static OLS estimates with the usual OLS d.f. corrected coefficient covariance. Cross-references See Chapter 26. “Cointegrating Regression,” beginning on page 255 of User’s Guide II for a discussion of single equation cointegrating regression. See Chapter 18. “Panel Cointegration Estimation,” beginning on page 911 of User’s Guide II for discussion of estimation in panel settings. See “Vector Error Correction (VEC) Models” on page 663 of User’s Guide II for a discussion of VEC estimation.
- 334. copy—327 See also coint (p. 311). Add comment to command capture window. Syntax commandcap arg sends arg to the command capture window. Of particular use to add-in writers for sending notifications to users. Cross-references See “Command Capture” on page 817 of the User’s Guide I for further details. See also statusline (p. 482). Copy an object, or a set of objects matching a name pattern, within and between workfiles, workfile pages, and databases. Data in series objects may be frequency converted or match merged. Syntax copy(options) src_spec dest_spec [src_id dest_id] copy(options) src_spec dest_spec [@src src_ids @dest dest_id] where src_spec and dest_spec are of the form: [ctype][container::][page]object_name There are three parts to the copy command: (1) a specification of the location and names of the source objects; (2) a specification of the location and names of the destination objects; (3) optional source and destination IDs if the copy operation involves match merging. The source and destination objects are specified in multiple (optional) parts: (1) the con- tainer specification is the name of a workfile or database; (2) the page specification is the name of a page within a workfile or a subdirectory within a database; and (3) the object_name specification is the name of an object or a wildcard pattern corresponding to multiple objects. The ctype specification is rarely required, but permits you to specify precisely your source or destination in cases where a database and workfile share the same name. In this case, ctype may be used to indicate the container to which you are referring by prefixing the container commandcap Programming Commands copy Object Container, Data, and File Commands
- 335. 328—Chapter 12. Command Reference name with “:” to indicate the workfile, or “::” to indicate the database with the common name. When parts of the source or destination specification are not provided, EViews will fill in default values where possible. The default container is the active workfile, unless the “::” prefix is used in which case the default container is the default database. The default page within a workfile is always the active page. The default name for the destination object is the name of the object within the source container. If ID series are not provided in the command, then EViews will perform frequency conver- sion when copying data whenever the source and destination containers have different fre- quencies. If ID series are provided, then EViews will perform a general match merge between the source and destination using the specified ID series. In the case where you wish to copy your data using match merging with special treatment for date matching, you must use the special keyword “@DATE” as an ID series for the source or destination. If “@DATE” is not specified as an identifier in either the source or destination IDs, EViews will perform an exact match merge using the given identifiers. If ID series are not specified, but a conversion option requiring a general match merge is used (e.g., “c=med”), “@DATE @DATE” will be appended to the list of IDs and a general date match merge will be employed. See Link::linkto (p. 353) in the Object Reference for additional discussion of the differ- ences embodied in these choices. The general syntax described above covers all possible uses of the copy command. The fol- lowing paragraphs provide examples of the specific syntax used for some common cases of the command. Copying Within a Workfile Copy an object within the default workfile page as a new object with a different name: • copy(options) src_name dest_name Copy an object from the src_page page into the default workfile page using the specified name: • copy(options) src_pagesrc_name dest_name Copy an object from the src_page page into the dest_page page, keeping the same name: • copy(options) src_pagesrc_names dest_page Copy an object from the src_page page to the default workfile page, match merging any series data using a single src_id and a single dest_id identifier series: • copy(options) src_pagesrc_name dest_name src_id dest_id
- 336. copy—329 Copy an object from the src_page page to the dest_page match merging any series data using multiple source and destination identifier series: • copy(options) src_pagesrc_name dest_pagedest_name @src src_id1 src_id2 ... src_id_n @dest dest_id1 dest_id2 ... dest_id_n Copying Between Containers (Workfiles and Databases) Copy one or more objects from the src_page of the workfile src_workfile to the dest_page of the workfile dest_workfile, using the name or name pattern given in src_names: • copy(options) src_workfile::src_pagesrc_names dest_workfile::dest_page Copy an object from database src_database to the default page in the container dest_con- tainer: • copy(options) src_database::src_name dest_container::dest_name Note that if both a workfile and database exist matching the name provided in dest_con- tainer, EViews will favor the workfile unless the “::” prefix is used to specify explicitly that the database should be used. Options Basic Options overwrite / o Overwrite any existing object with the destination name in the destination container. Error only if a non-editable series is encountered in the destination location. merge / m If the source object is a series, merge the data from the source series into any existing destination series, preserv- ing any values in the destination series that are not present in the source. For all other object types, overwrite any existing object with the source object. Error if a non-edit- able series is encountered in the destination location. protect / p Protect objects in the destination location from overwriting or merging. If there is an existing object in the destination container, cancel the copy operation for that object, but do not generate an error. noerr Suppress errors that are generated during the copy. For example, if the overwrite option is used, suppress any error caused by attempting to overwrite a non-editable series such as an index series used in the workfile structure. link Link the object to the source data so that the values can be refreshed at a later time.
- 337. 330—Chapter 12. Command Reference Group Copy Options When copying a group object from workfile to database: When copying a group object from a database to a workfile: Note that copying a group object containing expressions or auto-updating series between workfiles only copies the expressions, and not the underlying series. Frequency Conversion Options If the copy command does not specify identifier series, EViews will perform frequency con- version of the data contained in series objects whenever the source and destination contain- ers are dated, but do not have the same frequency. If either of the pages are undated, EViews will, unless match merge options are provided (as described below), perform a raw copy, in which the first observation in the source workfile page is copied into the first observation in the destination page, the second observation in the source into the second observation in the destination, and so forth. The following options control the frequency conversion method when copying series and group objects into a workfile page and converting from low to high frequency: g=arg Method for copying group objects from a workfile to data- base: “s” (copy group definition and series as separate objects), “t” (copy group definition and series as one object), “d” (copy series only as separate objects), “l” (copy group definition only). g=arg Method for copying group objects from a database or work- file to a workfile: “b” (copy both group definition and series), “d” (copy only the series), “l” (copy only the group definition). c=arg Low to high conversion methods: “r” (constant match aver- age), “d” (constant match sum), “q” (quadratic match average), “t” (quadratic match sum), “i” (linear match last), “c” (cubic match last), “pointf” (point first), “pointl” (point last), “dentonf” (Denton first), “dentonl” (Denton last), “dentona” (Denton average), “dentons” (Denton sum), “chowlinf” (Chow-Lin first), “chowlinl” (Chow-Lin last), “chowlina” (Chow-Lin average),“chowlins” (Chow- Lin sum), “litmanf” (Litterman first), “litmanl” (Litterman last), “litmana” (Litterman average), “litmans” (Litterman sum). rho=arg Autocorrelation coefficient (for Chow-Lin and Litterman conversions). Must be between 0 and 1, inclusive.
- 338. copy—331 In addition, for Denton, Chow-Lin, and Litterman conversions, you must specify the indica- tor series by appending the keyword “@indicator” followed by the series name or names at the end of the copy command. The following options control the frequency conversion method when copying series and group objects into a workfile page and converting from high to low frequency: Note that if no conversion method is given in the command, the conversion method specified within the series object will be used as the default. If the series does not contain an explicit conversion method, the global option settings will used to determine the method. Frequency conversion involving panel structured pages involves special handling: • If both pages are dated panel pages that are structured with a single identifier, EViews will perform frequency conversion cross-section by cross-section. • Conversion from a dated panel page to a dated, non-panel page will first perform a mean contraction across cross-sections to obtain a single time series (by computing the means for each period), and then a frequency conversion of the resulting time series to the new frequency. • Conversion from a dated, non-panel page to a dated panel page will first involve a fre- quency conversion of the single time series to the new frequency. The converted time series will be used for each cross-section in the panel page. In all three of these cases, all of the high-to-low conversion methods are supported, but low- to-high frequency conversion only offers Constant-match average (repeating of the low fre- quency observations). Lastly, conversion involving a panel page with more than one dimension or an undated page will default to raw data copy unless general match merge options are provided. Match Merge Options These options are available when ID series are specified in the copy commmand. c=arg High to low conversion methods removing NAs: “a” (aver- age of the nonmissing observations), “s” (sum of the non- missing observations), “f” (first nonmissing observation), “l” (last nonmissing observation), “x” (maximum nonmiss- ing observation), “m” (minimum nonmissing observation). High to low conversion methods propagating NAs: “an” or “na” (average, propagating missings), “sn” or “ns” (sum, propagating missings), “fn” or “nf” (first, propagating missings), “ln” or “nl” (last, propagating missings), “xn” or “nx” (maximum, propagating missings), “mn” or “nm” (minimum, propagating missings).
- 339. 332—Chapter 12. Command Reference Most of the conversion options should be self-explanatory. As for the others: “first” and “last” give the first and last non-missing observed for a given group ID; “obs” provides the number of non-missing values for a given group; “nas” reports the number of NAs in the group; “unique” will provide the value in the source series if it is the identical for all obser- vations in the group, and will return NA otherwise; “none” will cause the copy to fail if there are multiple observations in any group—this setting may be used if you wish to pro- hibit all contractions. On a match merge expansion, copying with match merging will repeat the value of the source for every observation with matching identifier values in the destination. If both the source and destination have multiple values for a given ID, EViews will first perform a con- traction across IDs in the source (if not ruled out by “c=none”), and then perform the expansion by replicating the contracted value in the destination. For example, converting from a quarterly panel to an annual panel using match merge, EViews will first contract the data across IDs down to a single quarterly time series, will convert the series to an annual smpl= smpl_spec Sample to be used when computing contractions during copying using match merge. Either provide the sample range in double quotes or specify a named sample object. By default, EViews will use the entire workfile sample “@ALL”. c=arg Set the match merge contraction method. If you are copying a numeric source series by general match merge, the argument can be one of: “mean”, “med” (median), “max”, “min”, “sum”, “sumsq” (sum-of- squares), “var” (variance), “sd” (standard deviation), “skew” (skewness), “kurt” (kurtosis), “quant” (quantile, used with “quant=” option), “obs” (number of observa- tions), “nas” (number of NA values), “first” (first observa- tion in group), “last” (last observation in group), “unique” (single unique group value, if present), “none” (disallow contractions). If copying an alpha series, only the non-summary methods “max”, “min”, “obs”, “nas”, first”, “last”, “unique” and “none” are supported. For copying of numeric series, the default contraction method is “c=mean”; for copying of alpha series, the default is “c=unique”. quant=number Quantile value to be used when contracting using the “c=quant” option (e.g, “quant=.3”). nacat Treat “NA” values as a category when copying using gen- eral match merge operations.
- 340. copy—333 frequency, then will assign the annual data to each of the cross-sections in the destination page. Examples copy good_equation best_equation makes an exact copy of GOOD_EQUATION and names it BEST_EQUATION. copy graph_1 wf2::wklygraph1 copies GRAPH_1 from the default page of the current workfile to GRAPH1 in the page WKLY of the workfile WF2. copy gdp usdat:: copies GDP from the current workfile to GDP in the USDAT database or workfile. copy ::gdp macro1::gdp_us copies GDP from the default database to either the open workfile MACRO1, or the database named MACRO1 if there is no open workfile with that name. If there is an open workfile MACRO1 you may use copy ::gdp ::macro1::gdp_us to specify explicitly that you wish to write to the MACRO1 database. copy(smpl="1990 2000") page1pop page2 @src county @date @dest county @date copies POP data for 1990 through 2005 from PAGE1 to PAGE2, match merge using the ids COUNTY and the date structure of the two pages. copy(smpl="1990 2000", c=mean) panelpageinc countypage county county copies the INC data from the PANELPAGE to the COUNTYPAGE, match merging using the values of the COUNTY series, and contracting the panel data by computing means for each county using the specified sample. copy countypagepop panelpage county county match merges the POP data from the COUNTYPAGE to the PANELPAGE using the values of the COUNTY series. copy(c=x, merge) quarterly::page1ser* annual::page6* copies all objects with names beginning with “SER” on page PAGE1 of workfile QUARTERLY into page PAGE6 of workfile ANNUAL using the existing names. Series objects with data that can be (high-to-low) frequency converted will take the maximum value within a low-fre- quency period as the conversion method. If destination series already exist with the same name as the source series, the data will be merged. If destination objects (non-series) exist with the same name as source series, they will be overwritten.
- 341. 334—Chapter 12. Command Reference Note that since databases are read from disk, you may provide a path for the database in the container specification, as in: copy "c:my datadba.edb::ser01" ser02 which copies the object SER01 from the database DBA.EDB located in the path “C:MY DATA” to SER02 in the default workfile page. copy gd* "c:my datafindat::" makes a duplicate of all objects in the default page of the current workfile with names start- ing with “GD” to the database FINDAT in the root of “C:MY DATA”. Cross-references See “Copying Objects” on page 315 of User’s Guide I for a discussion of copying and moving objects. See also fetch (p. 355), store (p. 484), and Link::linkto (p. 353) in the Object Refer- ence. Compute Pearson product-moment (ordinary) correlations for the specified series or groups. Syntax cor(options) arg1 [arg2 arg3...] where arg1, arg2, etc. are the names of series or groups. Note that this command is a limited feature version of the group view Group::cor (p. 298) in the Object Reference. Options cor Interactive Use Commands wgt=name (optional) Name of series containing weights. wgtmethod=arg (default = “sstdev”) Weighting method (when weights are specified using “weight=”): frequency (“freq”), inverse of variances (“var”), inverse of standard deviation (“stdev”), scaled inverse of variances (“svar”), scaled inverse of standard deviations (“sstdev”). pairwise Compute using pairwise deletion of observations with missing cases (pairwise samples).
- 342. count—335 Examples cor height weight age displays a Pearson correlation matrix for the three series HEIGHT, WEIGHT, and AGE. Cross-references See Group::cor (p. 298) in the Object Reference for more general routines for computing correlations. See also cov (p. 337). For simple functions to perform the calculations, see @cor (p. 664), and @cov (p. 665). Estimates models where the dependent variable is a nonnegative integer count. Syntax count(options) y x1 [x2 x3...] count(options) specification Follow the count keyword by the name of the dependent variable and a list of regressors. Options out=name Basename for saving output. All results will be saved in Sym matrices named using the string “CORR”, appended to the basename (e.g., the correlation specified by “out=my” is saved in the Sym matrix “MYCORR”). prompt Force the dialog to appear from within a program. p Print the result. count Interactive Use Commands d=arg (default=“p”) Likelihood specification: Poisson likelihood (“p”), normal quasi-likelihood (“n”), exponential likelihood (“e”), nega- tive binomial likelihood or quasi-likelihood (“b”). v=positive_num (default=1) Specify fixed value for QML parameter in normal and nega- tive binomial quasi-likelihoods. optmethod = arg Optimization method: “bfgs” (BFGS); “newton” (Newton- Raphson), “opg” or “bhhh” (OPG or BHHH), “legacy” (EViews legacy). Newton-Raphson is the default method. 3 3
- 343. 336—Chapter 12. Command Reference Examples The command: count(d=n,v=2,cov=glm) y c x1 x2 estimates a normal QML count model of Y on a constant, X1, and X2, with fixed variance parameter 2, and GLM standard errors. count arrest c job police makeresid(g) res_g estimates a Poisson count model of ARREST on a constant, JOB, and POLICE, and stores the generalized residuals in the series RES_G. optstep = arg Step method: “marquardt” (Marquardt); “dogleg” (Dog- leg); “linesearch” (Line search). Marquardt is the default method. cov=arg Covariance method: “ordinary” (default method based on inverse of the estimated information matrix), “huber” or “white” (Huber-White sandwich methods)., “glm” (GLM method).. covinfo = arg Information matrix method: “opg” (OPG); “hessian” (observed Hessian). (Applicable when non-legacy “optmethod=”.) h Huber-White quasi-maximum likelihood (QML) standard errors and covariances. (Legacy option Applicable when “optmethod=legacy”). g GLM standard errors and covariances. (Legacy option Applicable when “optmethod=legacy”). m=integer Set maximum number of iterations. c=scalar Set convergence criterion. The criterion is based upon the maximum of the percentage changes in the scaled coeffi- cients. The criterion will be set to the nearest value between 1e-24 and 0.2. s Use the current coefficient values in “C” as starting values (see also param (p. 453)). s=number Specify a number between zero and one to determine start- ing values as a fraction of the EViews default values (out of range values are set to “s=1”). prompt Force the dialog to appear from within a program. p Print the result.
- 344. cov—337 count(d=p) y c x1 fit yhat estimates a Poisson count model of Y on a constant and X1, and saves the fitted values (con- ditional mean) in the series YHAT. count(d=p, h) y c x1 estimates the same model with QML standard errors and covariances. Cross-references See “Count Models” on page 361 of User’s Guide II for additional discussion. See Equation::count (p. 79) of the Object Reference for the equivalent equation object command. Compute Pearson product-moment (ordinary) covariances for the specified series or groups. Syntax cor arg1 [arg2 arg3...] where arg1, arg2, etc. are the names of series or groups. Note that this command is a limited feature version of the group view Group::cov (p. 302) in the Object Reference. Options cov Interactive Use Commands wgt=name (optional) Name of series containing weights. wgtmethod=arg (default = “sstdev”) Weighting method (when weights are specified using “weight=”): frequency (“freq”), inverse of variances (“var”), inverse of standard deviation (“stdev”), scaled inverse of variances (“svar”), scaled inverse of standard deviations (“sstdev”). pairwise Compute using pairwise deletion of observations with missing cases (pairwise samples).
- 345. 338—Chapter 12. Command Reference Examples cov height weight age displays a Pearson covariance matrix for the three series HEIGHT, WEIGHT, and AGE. Cross-references See Group::cov (p. 302) in the Object Reference for more general routines for computing covariances. See also cor (p. 334) .For simple functions to perform the calculations, see @cor (p. 664), and @cov (p. 665). Create workfile. This command has been replaced by wfcreate (p. 510) and pagecreate (p. 434). Displays cross correlations (correlograms) for a pair of series. Syntax cross(n,options) ser1 ser2 [ser3 ...] You must specify the number of lags n to use in computing the cross correlations as the first option. EViews will create an untitled group from the specified series and groups, and will display the cross correlation view for the group. Options The following options may be specified inside the parentheses after the number of lags: out=name Basename for saving output. All results will be saved in Sym matrices named using the string “CORR”, appended to the basename (e.g., the correlation specified by “out=my” is saved in the sym matrix “MYCORR”). prompt Force the dialog to appear from within a program. p Print the result. create Object Container, Data, and File Commands cross Interactive Use Commands prompt Force the dialog to appear from within a program. p Print the cross correlogram. 3 3
- 346. data—339 Examples cross(36) log(m1) dlog(cpi) displays the cross correlogram between the log of M1 and the first difference of the log of CPI, using up to 36 leads and lags. equation eq1.arch sp500 c eq1.makeresid(s) res_std cross(24) res_std^2 res_std The first line estimates a GARCH(1,1) model and the second line retrieves the standardized residuals. The third line plots the cross correlogram squared standardized residual and the standardized residual, up to 24 leads and lags. This correlogram provides a rough check of asymmetry in the ARCH effect. Cross-references See “Cross Correlations and Correlograms” on page 559 of User’s Guide I for discussion. See Group::cross (p. 305) of the Object Reference for the equivalent group view command. Enter data from keyboard. Opens an unnamed group window to edit one or more series. Syntax data arg1 [arg2 arg3 ...] Follow the data keyword by a list of series and group names. You can list existing names or new names. Unrecognized names will cause new series to be added to the workfile. These series will be initialized with the value “NA”. Examples data group1 newx newy opens a group window containing the series in group GROUP1, and the series NEWX and NEWY. Cross-references See “Entering Data” on page 139 of User’s Guide I for a discussion of the process of entering data from the keyboard. data Object Creation Commands || Object Assignment Commands || Interac- tive Use Commands
- 347. 340—Chapter 12. Command Reference Open or create a database. If the specified database does not exist, a new (empty) database will be created and opened. The opened database will become the default database. Syntax db(options) [path]db_name [as shorthand_name] Follow the db command by the name of the database to be opened or to be created (if it does not already exist). You may include a path name to work with a database not in the default path. You may use the “as” keyword to provide an optional shorthand_name or short text label which may be used to refer to the database in commands and programs. If you leave this field blank, a default shorthand_name will be assigned automatically. See “Database Shorthands” on page 311 of User’s Guide I for additional discussion. Options See dbopen (p. 344) for a list of available options for working with foreign format data- bases. Examples db findat opens the database FINDAT in the default path and makes it the default database from which to store and fetch objects. If the database FINDAT does not already exist, an empty database named FINDAT will be created and opened. Cross-references See Chapter 10. “EViews Databases,” on page 307 of User’s Guide I for a discussion of EViews databases. See also dbcreate (p. 342) and dbopen (p. 344). Make a copy of an existing database. Syntax dbcopy [path]source_name [path]copy_name db Object Container, Data, and File Commands dbcopy Object Container, Data, and File Commands
- 348. dbcopy—341 Follow the dbcopy command by the name of the existing database and a name for the copy. You should include a path name to copy from or to a database that is not in the default directory. All files associated with the database will be copied. Options The following table summaries the various database formats, along with the corresponding allowable “type=” and “desttype=” keywords: For the source specification, the following options may be required when connecting to a remote server: For the destination specification, the following options may be required when connecting to a remote server: type=arg Specify the source database type: (see table below). The default is to read an EViews 7 database. desttype=arg, t=arg Specify the destination database type: (see table below). The default is to create an EViews 7 database. Option Keywords Aremos-TSD x “a”, “aremos”, “tsd” DRIBase x “b” “dribase” DRIPro Link x “dripro” DRI DDS “dds” EViews “e”, “evdb” EViews 6 compatible “eviews6” FAME “f”, “fame” GiveWin/PcGive “g”, “give” RATS 4.x “r”, “rats” RATS Portable / TROLL “l”, “trl” TSP Portable “t”, “tsp” s=server_id, server=server_id Server name u=user, username=user Username p=pwd, password=pwd Password
- 349. 342—Chapter 12. Command Reference Examples dbcopy usdat c:backupusdat makes a copy of all files associated with the database USDAT in the default path and stores it in the “c:backup” directory under the basename “Usdat”. Cross-references See Chapter 10. “EViews Databases,” on page 307 of User’s Guide I for a discussion of EViews databases. See also dbrename (p. 347) and dbdelete (p. 344). Create a new database. Syntax dbcreate(options) [path]db_name [as shorthand_name] Follow the dbcreate keyword by a name for the new database. You may include a path name to create a database not in the default directory. The new database will become the default database. You may use the “as” keyword to provide an optional shorthand_name or a short text label which may be used to refer to the open database in commands and programs. If you leave this field blank, a default shorthand_name will be assigned automatically. See “Database Shorthands” on page 311 of the User’s Guide I for additional discussion. Options dests=server_id, destserver =server_id Server name destu=user, destusername=us er Username destp=pwd, destpassword =pwd Password dbcreate Object Container, Data, and File Commands type=arg, t=arg Specify the database type: (see table below). The default is to create an EViews 7 database.
- 350. dbcreate—343 The following table summaries the various database formats, along with the corresponding “type=” keywords: You must have EViews Enterprise Edition to create DRIBase and FAME databases. The following options may be required when connecting to a remote server: Examples dbcreate macrodat creates a new database named MACRODAT in the default path, and makes it the default database from which to store and fetch objects. This command will issue an error message if a database named MACRODAT already exists. To open an existing database, use dbopen (p. 344) or db (p. 340). Cross-references See Chapter 10. “EViews Databases,” on page 307 of User’s Guide I for a discussion of EViews databases. See also dbopen (p. 344) or db (p. 340). Option Keywords Aremos-TSD x “a”, “aremos”, “tsd” DRIBase x “b” “dribase” DRIPro Link x “dripro” DRI DDS “dds” EViews “e”, “evdb” EViews 6 compatible “eviews6” FAME “f”, “fame” GiveWin/PcGive “g”, “give” RATS 4.x “r”, “rats” RATS Portable / TROLL “l”, “trl” TSP Portable “t”, “tsp” s=server_id, server=server_id Server name u=user, username=user Username p=pwd, password=pwd Password
- 351. 344—Chapter 12. Command Reference Delete an existing database (all files associated with the specified database). Syntax dbdelete [path]db_name Follow the dbdelete keyword by the name of the database to be deleted. You may include a path name to delete a database not in the default path. Examples dbdelete c:temptestdat deletes all files associated with the TESTDAT database in the specified directory. Cross-references See Chapter 10. “EViews Databases,” on page 307 of User’s Guide I for a discussion of EViews databases. See also dbcopy (p. 340) and dbdelete (p. 344). Open an existing database. Syntax dbopen(options) [path]db_name [as shorthand_name] Follow the dbopen keyword with the name of a database. You should include a path name to open a database not in the default path. The opened database will become the default database. You do not need to specify a database name when opening a Datastream or FRED connection (“type=datastream” or “type=fred”) as EViews will automatically connect to the proper loca- tion. You may use the “as” keyword to provide an optional shorthand_name or a short text label which is used to refer to the open database in commands and programs. If you leave this field blank, a default shorthand_name will be assigned automatically. See “Database Shorthands” on page 311 of User’s Guide I for additional discussion. By default, EViews will use the extension of the database file to determine type. For exam- ple, files with the extension “.EDB” will be opened as an EViews database, while files with dbdelete Object Container, Data, and File Commands dbopen Object Container, Data, and File Commands
- 352. dbopen—345 the extension “.IN7” will be opened as a GiveWin database. You may use the “type=” option to specify an explicit type. Options The following table summaries the various database formats, along with the corresponding “type=” keywords: You must have EViews Enterprise Edition to access Bloomberg, CEIC, Datastream, DRIBase, EcoWin, EIA, FactSet, FAME, Haver, IHS Global Insight, IHS Magellan, and Moody’s Econ- omy.com databases. type=arg, t=arg Specify the database type: (see table below). Option Keywords AREMOS Bank “aremos AREMOS TSD “a”, “tsd” Bloomberg “bloom” CEIC “ceic” Datastream “datastream” DRIBase “b” “dribase” DRIPro Link “dripro” DRI DDS “dds” EIA Bulk File “eiabulk” EIA Online “eia” EcoWin “ecowin” EViews “e”, “eviews” FactSet “factset” FAME “f”, “fame” FRED “fred” GiveWin/PcGive “g”, “give” Haver “h”, “haver” IHS Global Insight “ihs global insight” IHS Magellan “magellan” Moody’s Economy.com “economy” RATS 4.x “r”, “rats” RATS Portable / TROLL “l”, “trl” TSP Portable “t”, “tsp”
- 353. 346—Chapter 12. Command Reference The use of Bloomberg, CEIC, Datastream, DRIPro Link, EcoWin, EIA, FactSet, FRED, IHS IHS Global Insight, Magellan, and Economy.com databases requires an active connection to the internet. In addition, specific types may require installation of additional software. For details see, “Notes on Particular Formats” on page 337 in User’s Guide I. The following options may be required when connecting to a remote server: Examples dbopen c:dataus1 opens a database named US1 in the C:DATA directory. The command: dbopen us1 opens a database in the default path. If the specified database does not exist, EViews will issue an error message. You should use db (p. 340) or dbcreate (p. 342) to create a new database. Cross-references See Chapter 10. “EViews Databases,” on page 307 of User’s Guide I for a discussion of EViews databases. See also db (p. 340) and dbcreate (p. 342). Pack an existing database. Syntax dbpack [path]db_name Follow the dbpack keyword by a database name. You may include a path name to pack a database not in the default path. Examples dbpack findat s=server_id, server=server_id Server name u=user, username=user Username p=pwd, password=pwd Password dbpack Object Container, Data, and File Commands
- 354. dbrename—347 packs the database named FINDAT in the default path. Cross-references See “Packing the Database” on page 334 of User’s Guide I for additional discussion. See also dbrebuild (p. 347). Rebuild an existing database. Rebuild a seriously damaged database into a new database file. Syntax dbrebuild [path]source_name [path]dest_name Follow the dbrebuild keyword by the name of the database to be rebuilt, and then a new database name. Examples If you issue the command: dbrebuild testdat fixed_testdat EViews will attempt to rebuild the database TESTDAT into the database FIXED_TESTDAT in the default directory. Cross-references See “Maintaining the Database” on page 333 of User’s Guide I for a discussion. See also dbpack (p. 346). Rename an existing database. dbrename renames all files associated with the specified database. Syntax dbrename [path]old_name [path]new_name Follow the dbrename keyword with the current name of an existing database and the new name for the database. dbrebuild Object Container, Data, and File Commands dbrename Object Container, Data, and File Commands
- 355. 348—Chapter 12. Command Reference Examples dbrename testdat mydat Renames all files associated with the TESTDAT database in the specified directory to MYDAT in the default directory. Cross-references See Chapter 10. “EViews Databases,” on page 307 of User’s Guide I for a discussion of EViews databases. See db (p. 340) and dbcreate (p. 342). See also dbcopy (p. 340) and dbdelete (p. 344). Deletes objects from a workfile or a database. Syntax delete(options) arg1 [arg2 arg3 ...] Follow the keyword by a list of the names of any objects you wish to remove from the cur- rent workfile. Deleting does not remove objects that have been stored on disk in EViews database files. Options You can delete an object from a database by prefixing the name with the database name and a double colon. You can use a pattern to delete all objects from a workfile or database with names that match the pattern. Use the “?” to match any one character and the “*” to match zero or more characters. If you use delete in a program file, EViews will delete the listed objects without prompting you to confirm each deletion. Examples To delete all objects in the workfile with names beginning with “TEMP”, you may use the command: delete temp* To delete the objects CONS and INVEST from the database MACRO1, use: delete macro1::cons macro1::invest delete Object Utility Commands noerr Do not error if the object doesn’t exist.
- 356. driconvert—349 Cross-references See “Object Commands” on page 17 for a discussion of working with objects. See Chapter 10. “EViews Databases,” on page 307 of User’s Guide I for a discussion of EViews databases. Execute without opening window. Syntax do procedure do is most useful in EViews programs where you wish to run a series of commands without opening windows in the workfile area. Examples output(t) c:resultjunk1 do gdp.adf(c, 4, p) The first line redirects table output to a file on disk. The second line carries out a unit root test of GDP without opening a window, and prints the results to the disk file. Cross-references See “Object Commands” on page 17 for a discussion of working with objects. Chapter 1. “Object View and Procedure Reference,” on page 2 in the Object Reference pro- vides a complete listing of the views of the various objects. See also show (p. 473). Convert the entire DRI Basic Economics database into an EViews database. You must create an EViews database to store the converted DRI data before you use this command. This command may be very time-consuming. Syntax driconvert db_name Follow the command by listing the name of an existing EViews database into which you would like to copy the DRI data. You may include a path name to specify a database not in the default path. do Command Actions driconvert Object Container, Data, and File Commands
- 357. 350—Chapter 12. Command Reference Examples dbcreate dribasic driconvert dribasic driconvert c:mydatadridbase The first line creates a new (empty) database named DRIBASIC in the default directory. The second line copies all the data in the DRI Basic Economics database into in the DRIBASIC database. The last example copies the DRI data into the database DRIDBASE that is located in the C:MYDATA directory. Cross-references See Chapter 10. “EViews Databases,” on page 307 of the User’s Guide I for a discussion of EViews databases. See also dbcreate (p. 342) and db (p. 340). Execute a program. The exec command executes a program. The program may be located in memory or stored in a program file on disk. Syntax exec(options) [path]prog_name(prog_options) [%0 %1 …] If you wish to pass one or more options to the program, you should enclose them in paren- theses immediately after the filename. If the program has arguments, you should list them after the filename. EViews first checks to see if the specified program is in memory. If the program is not located, EViews then looks for the program on disk in the EViews Add-ins directory, or in the specified path. The program file should have a “.PRG” extension, which you need not spec- ify in the prog_name. exec Programming Commands
- 358. exec—351 Options Examples exec rollreg will run the program “Rollreg.prg” in the EViews add-in directory. exec(this=graph01) recshade will run the program “Recshade” in the EViews add-in directory, setting the _this object to GRAPH01. exec(4) c:myfilessimul.prg(h=3) xhat will run the program “Simul.prg” in the path “c:myfiles”, with program option string “h=3”, the %0 argument set to “XHAT”, and with the maximum error count set to 4. Note that in contrast to the run command, exec will not stop executing a running program after returning from the executed program. For example if you have a program containing: exec simul print x integer (default=1) Set maximum errors allowed before halting the program in interactive mode. Note that the integer option does not apply when using exec in a program, it only applies when using exec from the command line. When using exec in a parent program to execute a child program, the child program inherits the maximum error count from the parent. c Run program file without opening a window for display of the program file. verbose / quiet Verbose mode in which messages will be sent to the status line at the bottom of the EViews window (slower execu- tion), or quiet mode which suppresses workfile display updates (faster execution). v / q Same as [verbose / quiet]. ver4 / ver5 Execute program in [version 4 / version 5] compatibility mode. this=object_name Set the _this object for the executed program. If omitted, the executed program will inherit the _this object from the parent program, or from the current active workfile object when the exec command is issued from the com- mand window.
- 359. 352—Chapter 12. Command Reference the print statement will be executed after running the “Simul.prg” program. If you replace exec with run, the program will stop after executing the commands in “Simul.prg”. Cross-references See “Executing a Program” on page 120 of the User’s Guide I and “The Active Object Key- word” on page 209 in the Command and Programming Reference for further details. See also run (p. 467) and include (p. 713). Exit from EViews (close the EViews application). You will be prompted to save objects and workfiles which have changed since the last time they were saved to disk. Be sure to save your workfiles, if desired, since all changes that you do not save to a disk file will be lost. Syntax exit Cross-references See also close (p. 310) and save (p. 468). Expand a workfile. No longer supported. See the replacement command pagestruct (p. 448). Factor breakpoint test for stability. Carries out a factor breakpoint test for parameter constancy. Syntax facbreak(options) ser1 [ser2 ser3 ...] @ x1 x2 x3 You must provide one or more series to be used as the factors with which to split the sample into categories. To specify more than one factor, separate the factors by a space. If the equa- tion is specified by list and contains no linear terms, you may specify a subset of the regres- sors to be tested for a breakpoint after an “@” sign. exit Global Commands expand Object Container, Data, and File Commands facbreak Interactive Use Commands
- 360. factest—353 Options Examples The commands ls log(spot) c log(p_us) log(p_uk) facbreak season perform a regression of the log of SPOT on a constant, the log of P_US, and the log of P_UK, and employ a factor breakpoint test to determine whether the parameters are stable through the different values of SEASON. To test whether only the constant term and the coefficient on the log of P_US are “stable” enter the commands: facbreak season @ c log(p_us) Cross-references See “Factor Breakpoint Test” on page 179 of User’s Guide II for further discussion. See also Equation::facbreak (p. 86) and Equation::rls (p. 153) in the Object Refer- ence. Specify and estimate a factor analysis model. Syntax factest(method=arg, options) x1 [x2 x3...] [@partial z1 z2 z3...] factest(method=arg, options) matrix_name [[obs] [conditioning]] [@ name1 name2 name3...] where: and the available options are specific to the factor estimation method (see “Factor Methods” on page 179 of the Object Reference). p Print the result of the test. factest Interactive Use Commands method=arg (default= “ml”) Factor estimation method: “ml” (maximum likelihood), “gls” (generalized least squares), “ipf” (iterated principal factors), “pace” (non-iterative partitioned covariance matrix estimation), “pf” (principal factors), “uls” (unweighted least squares)
- 361. 354—Chapter 12. Command Reference The factest command allows you to estimated a factor analysis model without first declar- ing a factor object and then applying an estimation method. It provides a convenient method of interactively estimating transitory specifications that are not to be named and saved with the workfile. Estimation of a factor analysis specification using factest only differs from estimation using a named factor and a factor estimation procedure (e.g., Factor::ipf (p. 193) in the Object Reference) in the use of the “method=” option and in the fact that the command results in an unnamed factor object. Examples The command: factest(method=gls) g1 estimates a factor analysis model for the series in G1 using GLS. The result is an unnamed factor object. (Almost) equivalently, we may declaring and estimate the factor analysis object using the Factor::gls estimation method procedure factor f1.gls g1 which differs only in the fact that the former yields an unnamed factor object and the latter saves the object F1 in the workfile. The command: factest(method=ml) group01 @partial ser1 ser2 estimates the factor model using the partial correlation for the series in GROUP01, condi- tional on the series SER1 and SER2. The command is equivalent to: factor f2.ml group01 @partial ser1 ser2 except the latter names the factor object F2. Cross-references See Chapter 21. “Factor Analysis,” on page 981 of User’s Guide II for a general discussion of factor analysis. The various estimation methods are described in “Estimation Methods” on page 1014 of User’s Guide II. See Factor::gls (p. 189), Factor::ipf (p. 193), Factor::ml (p. 202), Factor::pf (p. 211), and Factor::uls (p. 225) all in the Object Reference.
- 362. fetch—355 Fetch objects from databases or databank files into the workfile. fetch reads one or more objects from EViews databases or databank files into the active workfile. The objects are loaded into the workfile using the object in the database or using the databank file name. If you fetch a series into a workfile with a different frequency, EViews will automatically apply the frequency conversion method attached to the series by setconvert. If the series does not have an attached conversion method, EViews will use the method set by Options/Date-Frequency in the main menu. You can override the conversion method by specifying an explicit conversion method option. Syntax fetch(options) object_list The fetch command keyword is followed by a list of object names separated by spaces. The default behavior is to fetch the objects from the default database (this is a change from ver- sions of EViews prior to EViews 3.x where the default was to fetch from individual databank files). You can precede the object name with a database name and the double colon “::” to indicate a specific database source. If you specify the database name as an option in parentheses (see below), all objects without an explicit database prefix will be fetched from the specified database. You may optionally fetch from individual databank files or search among regis- tered databases. You may use wild card characters, “?” (to match a single character) or “*” (to match zero or more characters), in the object name list. All objects with names matching the pattern will be fetched. To fetch from individual databank files that are not in the default path, you should include an explicit path. If you have more than one object with the same file name (for example, an equation and a series named CONS), then you should supply the full object file name including identifying extensions. Options fetch Object Container, Data, and File Commands d=db_name Fetch from specified database. d Fetch all registered databases in registry order. i Fetch from individual databank files.
- 363. 356—Chapter 12. Command Reference The following options are available for fetch of group objects: The database specified by the double colon “::” takes precedence over the database specified by the “d=” option. The following options control the frequency conversion method when copying series and group objects into a workfile page and converting from low to high frequency: In addition, for Denton, Chow-Lin, and Litterman conversions, you must specify the indica- tor series by appending the keyword “@indicator” followed by the series name or names at the end of the fetch command. link Fetch as a database link. notifyillegal When in a program, report illegal EViews object names. By default, objects with illegal names are automatically renamed. (Has no effect in the command window.) p=prefix Specify a naming prefix that will be prepended to the listed names to be fetched. s=suffix Specify a naming suffix that will be appended to the listed names to be fetched. pi=prefix Specify a naming prefix that will be removed from the name of the source objects once fetched into the workfile. si=suffix Specify a naming suffix that will be removed from the name of the source objects once fetched into the workfile. g=arg Group fetch options: “b” (fetch both group definition and series), “d” (fetch only the series in the group), “l” (fetch only the group definition). c=arg Low to high conversion methods: “r” (constant match aver- age), “d” (constant match sum), “q” (quadratic match average), “t” (quadratic match sum), “i” (linear match last), “c” (cubic match last), “pointf” (point first), “pointl” (point last), “dentonf” (Denton first), “dentonl” (Denton last), “dentona” (Denton average), “dentons” (Denton sum), “chowlinf” (Chow-Lin first), “chowlinl” (Chow-Lin last), “chowlina” (Chow-Lin average),“chowlins” (Chow- Lin sum), “litmanf” (Litterman first), “litmanl” (Litterman last), “litmana” (Litterman average), “litmans” (Litterman sum).. rho=arg Autocorrelation coefficient (for Chow-Lin and Litterman conversions). Must be between 0 and 1, inclusive.
- 364. fetch—357 The following options control the frequency conversion method when copying series and group objects to a workfile, converting from high to low frequency: If no conversion method is specified, the series-specific or global default conversion method will be employed. Examples To fetch M1, GDP, and UNEMP from the default database, use: fetch m1 gdp unemp To fetch M1 and GDP from the US1 database and UNEMP from the MACRO database, use the command: fetch(d=us1) m1 gdp macro::unemp You can fetch all objects with names starting with “SP” by searching all registered databases in the search order. The “c=f” option uses the first (nonmissing) observation to convert the frequency of any matching series with a higher frequency than the destination workfile fre- quency: fetch(d,c=f) sp* You can fetch M1 and UNEMP from individual databank files using: fetch(i) m1 c:dataunemp To fetch all objects with names starting with “CONS” from the two databases USDAT and UKDAT, use the command: fetch usdat::cons* ukdat::cons* The command: fetch a?income will fetch all series beginning with the letter “A”, followed by any single character, and end- ing with the string “income”. c=arg High to low conversion methods removing NAs: “a” (aver- age of the nonmissing observations), “s” (sum of the non- missing observations), “f” (first nonmissing observation), “l” (last nonmissing observation), “x” (maximum nonmiss- ing observation), “m” (minimum nonmissing observation). High to low conversion methods propagating NAs: “an” or “na” (average, propagating missings), “sn” or “ns” (sum, propagating missings), “fn” or “nf” (first, propagating missings), “ln” or “nl” (last, propagating missings), “xn” or “nx” (maximum, propagating missings), “mn” or “nm” (minimum, propagating missings).
- 365. 358—Chapter 12. Command Reference Use the “notifyillegal” option to display a dialog when fetching the series MYIL- LEG@LNAME that will suggest a valid name and give you to opportunity to name the object before it is inserted into a workfile: fetch(notifyillegal) myilleg@lname The command: fetch(p=us_) gdp inv cons Fetches the series US_GDP, US_INV and US_CONS into workfile series with the same names. fetch(si=” us equity”) ibm msft Fetches the database series IBM US EQUITY, and MSFT US EQUITY into workfile series with names IBM and MSFT. Cross-references See Chapter 10. “EViews Databases,” on page 307 of User’s Guide I for a discussion of data- bases, databank files, and frequency conversion. Appendix A. “Wildcards,” on page 739 describes the use of wildcard characters. See also store (p. 484), and copy (p. 327). See Series::setconvert (p. 589) in the Object Reference for information on default con- version settings. Computes static forecasts or fitted values from an estimated equation. When the regressor contains lagged dependent values or ARMA terms, fit uses the actual values of the dependent variable instead of the lagged fitted values. You may instruct fit to compare the forecasted data to actual data, and to compute forecast summary statistics. (Note that we recommend that you instead use the equation proc Equation::fit since it explicitly specifies the equation of interest.) Not available for equations estimated using ordered methods; use Equation::makemodel to create a model using the ordered equation results. Syntax fit(options) yhat [y_se] Following the fit keyword, you should type a name for the forecast series and, optionally, a name for the series containing the standard errors and, for ARCH specifications, a name for the conditional variance series. fit Interactive Use Commands
- 366. fit—359 Forecast standard errors are currently not available for binary, censored, and count models. Options Examples equation eq1.ls cons c cons(-1) inc inc(-1) fit c_hat c_se genr c_up=c_hat+2*c_se genr c_low=c_hat-2*c_se line cons c_up c_low The first line estimates a linear regression of CONS on a constant, CONS lagged once, INC, and INC lagged once. The second line stores the static forecasts and their standard errors as C_HAT and C_SE. The third and fourth lines compute the +/- 2 standard error bounds. The fifth line plots the actual series together with the error bounds. equation eq2.binary(d=l) y c wage edu fit yf fit(i) xbeta genr yhat = 1-@clogit(-xbeta) The first line estimates a logit specification for Y with a conditional mean that depends on a constant, WAGE, and EDU. The second line computes the fitted probabilities, and the third line computes the fitted values of the index. The fourth line computes the probabilities from d In models with implicit dependent variables, forecast the entire expression rather than the normalized variable. u Substitute expressions for all auto-updating series in the equation. g Graph the fitted values together with the ±2 standard error bands. e Produce the forecast evaluation table. i Compute the fitted values of the index. Only for binary, censored and count models. s Ignore ARMA terms and use only the structural part of the equation to compute the fitted values. f = arg (default= “actual”) Out-of-fit-sample fill behavior: “actual” (fill observations outside the fit sample with actual values for the fitted vari- able), “na” (fill observations outside the fit sample with missing values). prompt Force the dialog to appear from within a program. p Print view.
- 367. 360—Chapter 12. Command Reference the fitted index using the cumulative distribution function of the logistic distribution. Note that YF and YHAT should be identical. Note that you cannot fit values from an ordered model. You must instead solve the values from a model. The following lines generate fitted probabilities from an ordered model: equation eq3.ordered y c x z eq3.makemodel(oprob1) solve oprob1 The first line estimates an ordered probit of Y on a constant, X, and Z. The second line makes a model from the estimated equation with a name OPROB1. The third line solves the model and computes the fitted probabilities that each observation falls in each category. Cross-references To perform dynamic forecasting, use forecast (p. 360). See Chapter 23. “Forecasting from an Equation,” on page 135 of User’s Guide II for a discus- sion of forecasting in EViews and Chapter 2. “Discrete and Limited Dependent Variable Models,” on page 315 of User’s Guide II for forecasting from binary, censored, truncated, and count models. See “Forecasting” on page 696 of User’s Guide II for a discussion of forecasting from sspace models. See Equation::forecast (p. 90) and Equation::fit (p. 87) in the Object Reference for the equivalent object commands. See Equation::makemodel (p. 126) and Model::solve (p. 445) in the Object Reference for forecasting from systems of equations or ordered equations. Computes (n-period ahead) dynamic forecasts of the default equation. forecast computes the forecast using the default equation for all observations in a speci- fied sample. In some settings, you may instruct forecast to compare the forecasted data to actual data, and to compute summary statistics. (Note that we recommend that you instead use the equation proc Equation::forecast since it explicitly specifies the equation of interest.) Syntax forecast(options) yhat [y_se] forecast Interactive Use Commands
- 368. forecast—361 You should enter a name for the forecast series and, optionally, a name for the series con- taining the standard errors. Forecast standard errors are currently not available for binary or censored models. forecast is not available for models estimated using ordered methods. Options Examples The following lines: smpl 1970q1 1990q4 equation eq1.ls con c con(-1) inc smpl 1991q1 1995q4 forecast con_d plot con_d estimate a linear regression over the period 1970Q1–1990Q4, computes dynamic forecasts for the period 1991Q1–1995Q4, and plots the forecast as a line graph. equation eq1.ls m1 gdp ar(1) ma(1) forecast m1_bj bj_se d In models with implicit dependent variables, forecast the entire expression rather than the normalized variable. u Substitute expressions for all auto-updating series in the equation. g Graph the forecasts together with the ±2 standard error bands. e Produce the forecast evaluation table. i Compute the forecasts of the index. Only for binary, cen- sored and count models. s Ignore ARMA terms and use only the structural part of the equation to compute the forecasts. b =arg MA backcast method: “fa” (forecast available). Only for equations estimated with MA terms. This option is ignored if you specify the “s” (structural forecast) option. The default method uses the estimation sample. f = arg (default= “actual”) Out-of-forecast-sample fill behavior: “actual” (fill observa- tions outside the forecast sample with actual values for the fitted variable), “na” (fill observations outside the forecast sample with missing values). prompt Force the dialog to appear from within a program. p Print view.
- 369. 362—Chapter 12. Command Reference forecast(s) m1_s s_se plot bj_se s_se estimates an ARMA(1,1) model, computes the forecasts and standard errors with and with- out the ARMA terms, and plots the two forecast standard errors. Cross-references To perform static forecasting, see fit (p. 358). See Chapter 23. “Forecasting from an Equation,” on page 135 of User’s Guide II for a discus- sion of forecasting in EViews and Chapter 2. “Discrete and Limited Dependent Variable Models,” on page 315 of User’s Guide II for forecasting from binary, censored, truncated, and count models. See “Forecasting” on page 696 of User’s Guide II for a discussion of forecasting from sspace models. See Equation::forecast (p. 90) and Equation::fit (p. 87) in the Object Reference, for the equivalent object commands. See Equation::makemodel (p. 126) and Model::solve (p. 445) in the Object Reference for forecasting from systems of equations or ordered equations. Creates graph, table, or text objects from a view. Syntax freeze(options, name) object_name.view_command If you follow the keyword freeze with an object name but no view of the object, freeze will use the default view for the object. You may provide a destination name for the object containing the frozen view in parentheses. Options Examples freeze gdp.uroot(4,t) creates an untitled table that contains the results of the unit root test of the series GDP. group rates tb1 tb3 tb6 freeze(gra1) rates.line(m) show gra1.align(2,1,1) freeze Command Actions mode = overwrite Overwrites the object name if it already exists.
- 370. frml—363 freezes a graph named GRA1 that contains multiple line graphs of the series in the group RATES, realigns the individual graphs, and displays the resulting graph. freeze(mygra) gra1 gra3 gra4 show mygra.align(2,1,1) creates a graph object named MYGRA that combines three graph objects GRA1, GRA2, and GRA3, and displays MYGRA in two columns. freeze(mode=overwrite, mygra) gra1 gra2 gra3 show mygra.align(2,1,1) creates a graph object MYGRA that combines the three graph objects GRA1, GRA2 and GRA3, and displays MYGRA in two columns. If the object MYGRA already exists, it would be replaced by the new object. Cross-references See “Object Commands” on page 17 for discussion. See also Chapter 4. “Object Basics,” on page 97 of User’s Guide I for further discussion of objects and views of objects. Freezing graph views is described in “Creating Graph Objects” on page 705 of User’s Guide I. Declare a series object with a formula for auto-updating, or specify a formula for an exist- ing series. Syntax frml series_name = series_expression frml series_name = @clear Follow the frml keyword with a name for the object, and an assignment statement. The special keyword “@CLEAR” is used to return the auto-updating series to an ordinary numeric or alpha series. Examples To define an auto-updating numeric or alpha series, you must use the frml keyword prior to entering an assignment statement. The following example creates a series named LOW that uses a formula to compute its values.: frml low = inc<=5000 or edu<13 The auto-updating series takes the value 1 if either INC is less than or equal to 5000 or EDU is less than 13, and 0 otherwise, and will be re-evaluated whenever INC or EDU change. If FIRST_NAME and LAST_NAME are alpha series, then the formula declaration: frml Object Creation Commands || Object Assignment Commands
- 371. 364—Chapter 12. Command Reference frml full_name = first_name + " " + last_name creates an auto-updating alpha series FULL_NAME. You may apply a frml to an existing series. The commands: series z = 3 frml z =(x+y)/2 makes the previously created series Z an auto-updating series containing the average of series X and Y. Note that once a series is defined to be auto-updating, it may not be modified directly. Here, you may not edit Z, nor may you generate values into the series. Note that the commands: series z = 3 z = (x+y)/2 while similar, produce quite different results, since the absence of the frml keyword in the second example means that EViews will generate fixed values in the series instead of defin- ing a formula to compute the series values. In this latter case, the values in the series Z are fixed, and may be modified. One particularly useful feature of auto-updating series is the ability to reference series in databases. The command: frml gdp = usdata::gdp creates a series called GDP that obtains its values from the series GDP in the database USDATA. Similarly: frml lgdp = log(usdata::gdp) creates an auto-updating series that is the log of the values of GDP in the database USDATA. To turn off auto-updating for a series or alpha, you should use the special expression “@CLEAR” in your frml assignment. The command: frml z = @clear sets the series to numeric or alpha value format, freezing the contents of the series at the current values. Cross-references See “Auto-Updating Series” on page 193 of User’s Guide I for a discussion of updating series. See also Link::link (p. 352) in the Object Reference.
- 372. glm—365 Generate series. Generate series or alphas. Syntax genr ser_name = expression Examples genr y = 3 + x generates a numeric series that takes the values from the series X and adds 3. genr full_name = first_name + last_name creates an alpha series formed by concatenating the alpha series FIRST_NAME and LAST_NAME. Cross-references See Chapter 6. “Working with Data,” on page 169 of User’s Guide I for discussion of generat- ing series data. See Series::series (p. 586) and Alpha::alpha (p. 6) in the Object Reference for a dis- cussion of the expressions allowed in genr. Estimate a Generalized Linear Model (GLM). Syntax glm(options) spec List the glm keyword, followed by the dependent variable and a list of the explanatory vari- ables, or an explicit linear expression. If you enter an explicit linear specification such as “Y=C(1)+C(2)*X”, the response vari- able will be taken to be the variable on the left-hand side of the equality (“Y”) and the linear predictor will be taken from the right-hand side of the expression (“C(1)+C(2)*X”). Offsets may be entered directly in an explicit linear expression or they may be entered as using the “offset=” option. genr Object Creation Commands || Object Assignment Commands glm Interactive Use Commands
- 373. 366—Chapter 12. Command Reference Specification Options family=arg (default=“normal”) Distribution family: Normal (“normal”), Poisson (“pois- son”), Binomial Count (“binomial”), Binomial Proportion (“binprop”), Negative Binomial (“negbin”), Gamma (“gamma”), Inverse Gaussian (“igauss”), Exponential Mean (“emean)”, Power Mean (“pmean”), Binomial Squared (“binsq”). The Binomial Count, Binomial Proportion, Negative Bino- mial, and Power Mean families all require specification of a distribution parameter: n=arg (default=1) Number of trials for Binomial Count (“family=binomial”) or Binomial Proportions (“family=binprop”) families. fparam=arg Family parameter value for Negative Binomial (“fam- ily=negbin”) and Power Mean (“family=pmean”) fami- lies. link=arg (default=“identity”) Link function: Identity (“identity”), Log (“log”), Log Com- pliment (“logc”), Logit (“logit”), Probit (“probit”), Log-log (“loglog”), Complementary Log-log (“cloglog”), Reciprocal (“recip”), Power (“power”), Box-Cox (“boxcox”), Power Odds Ratio (“opow”), Box-Cox Odds Ratio (“obox”). The Power, Box-Cox, Power Odds Ratio, and Box-Cox Odds Ratio links all require specification of a link parameter specified using “lparam=”. lparam=arg Link parameter for Power (“link=power”), Box-Cox (“link=boxcox”), Power Odds Ratio (“link=opow”) and Box-Cox Odds Ratio (“link=obox”) link functions. offset=arg Offset terms. disp=arg Dispersion estimator: Pearson statistic (“pearson”), deviance statistic (“deviance”), unit (“unit”), user-speci- fied (“user”). The default is family specific: “unit” for Binomial Count, Binomial Proportion, Negative Binomial, and Poison, and “pearson” for all others. The “deviance” option is only offered for families in the exponential family of distributions (Normal, Poisson, Bino- mial Count, Binomial Proportion, Negative Binomial, Gamma, Inverse Gaussian). dispval=arg User-dispersion value (if “disp=user”). fwgts=arg Frequency weights. w=arg Weight series or expression. x 2
- 374. glm—367 In addition to the specification options, there are options for estimation and covariance cal- culation. Additional Options wtype=arg (default=“istdev”) Weight specification type: inverse standard deviation (“ist- dev”), inverse variance (“ivar”), standard deviation (“stdev”), variance (“var”). wscale=arg Weight scaling: EViews default (“eviews”), average (“avg”), none (“none”). The default setting depends upon the weight type: “eviews” if “wtype=istdev”, “avg” for all others. optmethod = arg Optimization method: “bfgs” (BFGS); “newton” (Newton- Raphson), “opg” or “bhhh” (OPG or BHHH), “fisher” (IRLS – Fisher Scoring), “legacy” (EViews legacy). Newton-Raphson is the default method. optstep = arg Step method: “marquardt” (Marquardt); “dogleg” (Dog- leg); “linesearch” (Line search). Marquardt is the default method. estmeth=arg (default=”mar- quardt”) Legacy estimation algorithm: Quadratic Hill Climbing (“marquardt”), Newton-Raphson (“newton”), IRLS - Fisher Scoring (“irls”), BHHH (“bhhh”). (Applicable when “optmethod=legacy”.) m=integer Set maximum number of iterations. c=scalar Set convergence criterion. The criterion is based upon the maximum of the percentage changes in the scaled coeffi- cients. The criterion will be set to the nearest value between 1e-24 and 0.2. s Use the current coefficient values in estimator coefficient vector as starting values (see also param (p. 453) in the Command and Programming Reference). s=number Specify a number between zero and one to determine start- ing values as a fraction of EViews default values (out of range values are set to “s=1”). showopts / -showopts [Do / do not] display the starting coefficient values and estimation options in the estimation output. preiter=arg (default=0) Number of IRLS pre-iterations to refine starting values (only available for non-IRLS estimation).
- 375. 368—Chapter 12. Command Reference cov=arg Covariance method: “ordinary” (default method based on inverse of the estimated information matrix), “huber” or “white” (Huber-White sandwich method), “glm” (GLM method). covinfo = arg Information matrix method: “opg” (OPG); “hessian” (observed Hessian), “fisher” (expected Hessian). (Applicable when “optmethod=” not equal to “legacy”. nodf Do not degree-of-freedom correct the coefficient covariance estimate. covlag=arg (default=1) Whitening lag specification: integer (user-specified lag value), “a” (automatic selection). Applicable where “cov=hac”. covinfosel=arg (default=”aic”) Information criterion for automatic selection: “aic” (Akaike), “sic” (Schwarz), “hqc” (Hannan-Quinn) (if “lag=a”). For settings where “cov=hac, covlag=a”. covmaxlag=integer Maximum lag-length for automatic selection (optional) (if “lag=a”). The default is an observation-based maximum of . For settings where “cov=hac, covlag=a”. covkern=arg (default=“bart”) Kernel shape: “none” (no kernel), “bart” (Bartlett, default), “bohman” (Bohman), “daniell” (Daniel), “parzen” (Parzen), “parzriesz” (Parzen-Riesz), “parzgeo” (Parzen- Geometric), “parzcauchy” (Parzen-Cauchy), “quadspec” (Quadratic Spectral), “trunc” (Truncated), “thamm” (Tukey-Hamming), “thann” (Tukey-Hanning), “tparz” (Tukey-Parzen). For settings where “cov=hac”. covbw=arg (default=“fixednw”) Kernel Bandwidth: “fixednw” (Newey-West fixed), “andrews” (Andrews automatic), “neweywest” (Newey- West automatic), number (User-specified bandwidth). For settings where “cov=hac” and “covkern=” is speci- fied. covnwlag=integer Newey-West lag-selection parameter for use in nonpara- metric kernel bandwidth selection (if “covbw=newey- west”). For settings where “cov=hac” and “covkern=” is speci- fied. covbwoffset=number Apply offset to automatically selected bandwidth. For settings where “cov=hac”, “covkern=” is specified, and “covbw=” is not user-specified. T 1 3
- 376. glm—369 Examples glm(link=log) numb c ip feb estimates a normal regression model with exponential mean. glm(family=binomial, n=total) disease c snore estimates a binomial count model with default logit link where TOTAL contains the number of binomial trials and DISEASE is the number of binomial successes. The specification glm(family=binprop, n=total, cov=huber, nodf) disease/total c snore estimates the same specification in proportion form, and computes the coefficient covari- ance using the Huber-White sandwich with no d.f. correction. glm(family=binprop, disp=pearson) prate mprate log(totemp) log(totemp)^2 age age^2 sole estimates a binomial proportions model with default logit link, but computes the coefficient covariance using the GLM scaled covariance with dispersion computed using the Pearson Chi-square statistic. glm(family=binprop, link=probit, cov=huber) prate mprate log(totemp) log(totemp)^2 age age^2 sole estimates the same basic specification, but with a probit link and Huber-White standard errors. glm(family=poisson, offset=log(pyears)) los hmo white type2 type3 c estimates a Poisson specification with an offset term LOG(PYEARS). Cross-references See Chapter 3. “Generalized Linear Models,” beginning on page 375 of User’s Guide II for discussion. See Equation::glm (p. 92) in the Object Reference for the equivalent equation object com- mand. covbwint Use integer portion of kernel bandwidth. For settings where “cov=hac” and “covkern=” is speci- fied. coef=arg Specify the name of the coefficient vector (if specified by list); the default behavior is to use the “C” coefficient vec- tor. prompt Force the dialog to appear from within a program. p Print results.
- 377. 370—Chapter 12. Command Reference Estimation by generalized method of moments (GMM). Syntax gmm(options) y x1 [x2 x3...] @ z1 [z2 z3...] gmm(options) specification @ z1 [z2 z3...] Follow the name of the dependent variable by a list of regressors, followed by the “@” sym- bol, and a list of instrumental variables which are orthogonal to the residuals. Alternatively, you can specify an expression using coefficients, an “@” symbol, and a list of instrumental variables. There must be at least as many instrumental variables as there are coefficients to be estimated. In panel settings, you may specify dynamic instruments corresponding to predetermined variables. To specify a dynamic instrument, you should tag the instrument using “@DYN”, as in “@DYN(X)”. By default, EViews will use a set of period-specific instruments corre- sponding to lags from -2 to “-infinity”. You may also specify a restricted lag range using argu- ments in the “@DYN” tag. For example, to use lags from-5 to “-infinity” you may enter “@DYN(X, -5)”; to specify lags from -2 to -6, use “@DYN(X, -2, -6)” or “@DYN(X, -6, -2)”. Note that dynamic instrument specifications may easily generate excessively large numbers of instruments. Options Non-Panel GMM Options Basic GMM Options gmm Interactive Use Commands nocinst Do not include automatically a constant as an instrument. method=keyword Set the weight updating method. keyword should be one of the following: “nstep” (N-Step Iterative, or Sequential N- Step Iterative, default), “converge” (Iterate to Convergence or Sequential Iterate to Convergence), “simul” (Simultane- ous Iterate to Convergence), “oneplusone” (One-Step Weights Plus One Iteration), or “cue” (Continuously Updat- ing”. gmmiter=integer Number of weight iterations. Only applicable if the “method=nstep” option is set. w=arg Weight series or expression.
- 378. gmm—371 wtype=arg (default=“istdev”) Weight specification type: inverse standard deviation (“ist- dev”), inverse variance (“ivar”), standard deviation (“stdev”), variance (“var”). wscale=arg Weight scaling: EViews default (“eviews”), average (“avg”), none (“none”). The default setting depends upon the weight type: “eviews” if “wtype=istdev”, “avg” for all others. s Use the current coefficient values in estimator coefficient vector as starting values for equations specified by list with AR or MA terms (see also param (p. 453) of the Com- mand and Programming Reference). s=number Determine starting values for equations specified by list with AR or MA terms. Specify a number between zero and one representing the fraction of TSLS estimates computed without AR or MA terms to be used. Note that out of range values are set to “s=1”. Specifying “s=0” initializes coeffi- cients to zero. By default EViews uses “s=1”. Does not apply to coefficients for AR and MA terms which are instead set to EViews determined default values. m=integer Maximum number of iterations. c=number Set convergence criterion. The criterion is based upon the maximum of the percentage changes in the scaled coeffi- cients. The criterion will be set to the nearest value between 1e-24 and 0.2. l=number Set maximum number of iterations on the first-stage itera- tion to get the one-step weighting matrix. numericderiv / -numericderiv [Do / do not] use numeric derivatives only. If omitted, EViews will follow the global default. fastderiv / -fastderiv [Do / do not] use fast derivative computation. If omitted, EViews will follow the global default. showopts / -showopts [Do / do not] display the starting coefficient values and estimation options in the estimation output. coef=arg Specify the name of the coefficient vector (if specified by list); the default behavior is to use the “C” coefficient vec- tor. prompt Force the dialog to appear from within a program. p Print results.
- 379. 372—Chapter 12. Command Reference Estimation Weighting Matrix Options Covariance Options instwgt=keyword Set the estimation weighting matrix type. Keyword should be one of the following: “tsls” (two-stage least squares), “white” (White diagonal matrix), “hac” (Newey-West HAC, default) or “user” (user defined). instwgtmat=name Set the name of the user-defined estimation weighting matrix. Only applicable if the “instwgt=user” option is set. instlag=arg (default=1) Whitening Lag specification: integer (user-specified lag value), “a” (automatic selection). instinfosel=arg (default=“aic”) Information criterion for automatic whitening lag selection: “aic” (Akaike), “sic” (Schwarz), “hqc” (Hannan-Quinn) (if “instlag=a”). instmaxlag= integer Maximum lag-length for automatic selection (optional) (if “instlag=a”). The default is an observation-based maxi- mum of . instkern=arg (default=“bart”) Kernel shape: “none” (no kernel), “bart” (Bartlett, default), “bohman” (Bohman), “daniell” (Daniell), “parzen” (Parzen), “parzriesz” (Parzen-Riesz), “parzgeo” (Parzen- Geometric), “parzcauchy” (Parzen-Cauchy), “quadspec” (Quadratic Spectral), “trunc” (Truncated), “thamm” (Tukey-Hamming), “thann” (Tukey-Hanning), “tparz” (Tukey-Parzen). instbw=arg (default=“fixednw”) Kernel Bandwidth: “fixednw” (Newey-West fixed), “andrews” (Andrews automatic), “neweywest” (Newey- West automatic), number (User-specified bandwidth). instnwlag=integer Newey-West lag-selection parameter for use in nonpara- metric bandwidth selection (if “instbw=neweywest”). instbwint Use integer portion of bandwidth. cov=keyword Covariance weighting matrix type (optional): “updated” (estimation updated), “tsls” (two-stage least squares), “white” (White diagonal matrix), “hac” (Newey-West HAC), “wind” (Windmeijer) or “user” (user defined). The default is to use the estimation weighting matrix. nodf Do not perform degree of freedom corrections in computing coefficient covariance matrix. The default is to use degree of freedom corrections. covwgtmat=name Set the name of the user-definied covariance weighting matrix. Only applicable if the “covwgt=user” option is set. T 1 3
- 380. gmm—373 Panel GMM Options covlag=arg (default=1) Whitening lag specification: integer (user-specified lag value), “a” (automatic selection). covinfosel=arg (default=”aic”) Information criterion for automatic selection: “aic” (Akaike), “sic” (Schwarz), “hqc” (Hannan-Quinn) (if “lag=a”). covmaxlag=integer Maximum lag-length for automatic selection (optional) (if “lag=a”). The default is an observation-based maximum of . covkern=arg (default=“bart”) Kernel shape: “none” (no kernel), “bart” (Bartlett, default), “bohman” (Bohman), “daniell” (Daniel), “parzen” (Parzen), “parzriesz” (Parzen-Riesz), “parzgeo” (Parzen- Geometric), “parzcauchy” (Parzen-Cauchy), “quadspec” (Quadratic Spectral), “trunc” (Truncated), “thamm” (Tukey-Hamming), “thann” (Tukey-Hanning), “tparz” (Tukey-Parzen). covbw=arg (default=“fixednw”) Kernel Bandwidth: “fixednw” (Newey-West fixed), “andrews” (Andrews automatic), “neweywest” (Newey- West automatic), number (User-specified bandwidth). covnwlag=integer Newey-West lag-selection parameter for use in nonpara- metric kernel bandwidth selection (if “covbw=newey- west”). covbwint Use integer portion of bandwidth. cx=arg Cross-section effects method: (default) none, fixed effects estimation (“cx=f”), first-difference estimation (“cx=fd”), orthogonal deviation estimation (“cx=od”) per=arg Period effects method: (default) none, fixed effects estima- tion (“per=f”). levelper Period dummies always specified in levels (even if one of the transformation methods is used, “cx=fd” or “cx=od”). wgt=arg GLS weighting: (default) none, cross-section system weights (“wgt=cxsur”), period system weights (“wgt=persur”), cross-section diagonal weighs (“wgt=cxdiag”), period diagonal weights (“wgt=per- diag”). T 1 3
- 381. 374—Chapter 12. Command Reference gmm=arg GMM weighting: 2SLS (“gmm=2sls”), White period sys- tem covariances (Arellano-Bond 2-step/n-step) (“gmm=perwhite”), White cross-section system (“gmm=cxwhite”), White diagonal (“gmm=stackedwhite”), Period system (“gmm=persur”), Cross-section system (“gmm=cxsur”), Period heteroske- dastic (“cov=perdiag”), Cross-section heteroskedastic (“gmm=cxdiag”). By default, uses the identity matrix unless estimated with first difference transformation (“cx=fd”), in which case, uses (Arellano-Bond 1-step) difference weighting matrix. In this latter case, you should specify 2SLS weights (“gmm=2sls”) for Anderson-Hsiao estimation. cov=arg Coefficient covariance method: (default) ordinary, White cross-section system robust (“cov=cxwhite”), White period system robust (“cov=perwhite”), White heteroske- dasticity robust (“cov=stackedwhite”), Cross-section sys- tem robust/PCSE (“cov=cxsur”), Period system robust/PCSE (“cov=persur”), Cross-section heteroskedas- ticity robust/PCSE (“cov=cxdiag”), Period heteroskedastic- ity robust (“cov=perdiag”). keepwgts Keep full set of GLS/GMM weights used in estimation with object, if applicable (by default, only weights which take up little memory are saved). coef=arg Specify the name of the coefficient vector (if specified by list); the default behavior is to use the “C” coefficient vec- tor. iter=arg (default=“onec”) Iteration control for GLS and GMM weighting specifica- tions: perform one weight iteration, then iterate coefficients to convergence (“iter=onec”), iterate weights and coeffi- cients simultaneously to convergence (“iter=sim”), iterate weights and coefficients sequentially to convergence (“iter=seq”), perform one weight iteration, then one coef- ficient step (“iter=oneb”). s Use the current coefficient values in estimator coefficient vector as starting values for equations specified by list with AR or MA terms (see also param (p. 453) of the Com- mand and Programming Reference).
- 382. gmm—375 Note that some options are only available for a subset of specifications. Examples In a non-panel workfile, we may estimate equations using the standard GMM options. The specification: gmm(instwgt=white,gmmiter=2,nodf) cons c y y(-1) w @ c p(-1) k(-1) x(-1) tm wg g t estimates the Klein equation for consumption using GMM with a White diagonal weighting matrix (two steps and no degree of freedom correction). The command: gmm(method=cue,instwgt=hac,instlag=1,instkern=thann, instbw=andrews,nodf) i c y y(-1) k(-1) @ c p(-1) k(-1) x(-1) tm wg g t s=number Determine starting values for equations specified by list with AR terms. Specify a number between zero and one representing the fraction of TSLS estimates computed with- out AR terms to be used. Note that out of range values are set to “s=1”. Specifying “s=0” initializes coefficients to zero. By default EViews uses “s=1”. Does not apply to coefficients for AR terms which are instead set to EViews determined default values. m=integer Maximum number of iterations. c=number Set convergence criterion. The criterion is based upon the maximum of the percentage changes in the scaled coeffi- cients. The criterion will be set to the nearest value between 1e-24 and 0.2. l=number Set maximum number of iterations on the first-stage itera- tion to get the one-step weighting matrix. unbalsur Compute SUR factorization in unbalanced data using the subset of available observations for a cluster. numericderiv / -numericderiv [Do / do not] use numeric derivatives only. If omitted, EViews will follow the global default. fastderiv / -fastderiv [Do / do not] use fast derivative computation. If omitted, EViews will follow the global default. showopts / -showopts [Do / do not] display the starting coefficient values and estimation options in the estimation output. p Print results.
- 383. 376—Chapter 12. Command Reference estimates the Klein equation for investment using a Newey-West HAC weighting matrix, with pre-whitening with 1 lag, a Tukey-Hanning kernel and the Andrews automatic band- width routine. The estimation is performed using continuously updating weight iterations. When working with a workfile that has a panel structure, you may use the panel equation estimation options. The command gmm(cx=fd, per=f) dj dj(-1) @ @dyn(dj) estimates an Arellano-Bond “1-step” estimator with differencing of the dependent variable DJ, period fixed effects, and dynamic instruments constructed using DJ with observation specific lags from period to 1. To perform the “2-step” version of this estimator, you may use: gmm(cx=fd, per=f, gmm=perwhite, iter=oneb) dj dj(-1) @ @dyn(dj) where the combination of the options “gmm=perwhite” and (the default) “iter=oneb” instructs EViews to estimate the model with the difference weights, to use the estimates to form period covariance GMM weights, and then re-estimate the model. You may iterate the GMM weights to convergence using: gmm(cx=fd, per=f, gmm=perwhite, iter=seq) dj dj(-1) @ @dyn(dj) Alternately: gmm(cx=od, gmm=perwhite, iter=oneb) dj dj(-1) x y @ @dyn(dj,-2,-6) x(-1) y(-1) estimates an Arellano-Bond “2-step” equation using orthogonal deviations of the dependent variable, dynamic instruments constructed from DJ from period to , and ordi- nary instruments X(-1) and Y(-1). Cross-references See “Generalized Method of Moments” on page 69 and Chapter 17. “Panel Estimation,” on page 855 of User’s Guide II for discussion of the various GMM estimation techniques. See also tsls (p. 496). See Equation::gmm (p. 97) in the Object Reference for the equivalent equation object com- mand. Estimate a selection equation using the Heckman ML or 2-step method. Syntax heckit(options) response_eqn @ selection_eqn heckit Interactive Use Commands t 2– t 6– t 2–
- 384. heckit—377 The response equation should be the dependent variable followed by a list of regressors. The selection equation should be a binary dependent variable followed by a list of regressors. Options General Options ML Options Note these options are not available if the "2step" option, above, is used. 2step Use the Heckman 2-step estimation method. Note that this option is incompatible with the maximum likelihood options below. coef=arg Specify the name of the coefficient vector (if specified by list); the default behavior is to use the “C” coefficient vec- tor. prompt Force the dialog to appear from within a program. p Print the estimation results. optmethod = arg Optimization method: “bfgs” (BFGS); “newton” (Newton- Raphson), “opg” or “bhhh” (OPG or BHHH), “legacy” (EViews legacy). Newton-Raphson is the default method. optstep = arg Step method: “marquardt” (Marquardt); “dogleg” (Dog- leg); “linesearch” (Line search). Marquardt is the default method. cov=arg Covariance method: “ordinary” (default method based on inverse of the estimated information matrix), “huber” or “white” (Huber-White sandwich methods)., covinfo = arg Information matrix method: “opg” (OPG); “hessian” (observed Hessian). (Applicable when non-legacy “optmethod=”.) m=integer Set maximum number of iterations. c=number Set convergence criteria. numericderiv / -numericderiv [Do / do not] use numeric derivatives only. If omitted, EViews will follow the global default. showopts / -showopts [Do / do not] display the starting coefficient values and estimation options in the estimation output.
- 385. 378—Chapter 12. Command Reference Examples wfopen http://www.stern.nyu.edu/~wgreene/Text/Edition7/TableF5- 1.txt heckit ww c ax ax^2 we cit @ lfp c wa wa^2 faminc we (k618+kl6)>0 heckit(2step) ww c ax ax^2 we cit @ lfp c wa wa^2 faminc we (k618+kl6)>0 This example replicates the Heckman Selection example given in Greene (2008, page 888), which uses data from the Mroz (1987) study to estimate a selection model. The first line of this example downloads the data set, the second line creates an equation object and esti- mates it using the default maximum likelihood estimation method of Heckman Selection, which replicates the first pane of Table 24.3 in Greene. The third line estimates the same model, using the two-step approach, which replicates the second pane of Table 24.3. Cross-references Convert an entire Haver Analytics Database into an EViews database. Syntax hconvert haver_path db_name You must have a Haver Analytics database installed on your computer to use this feature. You must also create an EViews database to store the converted Haver data before you use this command. Be aware that this command may be very time-consuming. Follow the command by a full path name to the Haver database and the name of an existing EViews database to store the Haver database. You can include a path name to the EViews database not in the default path. Examples dbcreate hdata hconvert d:haverhaver hdata The first line creates a new (empty) database named HDATA in the default directory. The second line converts all the data in the Haver database and stores it in the HDATA database. s=number Scale EViews’ starting values by number. r Use Newton-Raphson optimizer. b Use BHHH optimizer. hconvert Object Container, Data, and File Commands
- 386. hfetch—379 Cross-references See Chapter 10. “EViews Databases,” on page 307 of User’s Guide I for a discussion of EViews and Haver databases. See also dbcreate (p. 342), db (p. 340), hfetch (p. 379) and hlabel (p. 380). Fetch a series from a Haver Analytics database into a workfile. hfetch reads one or more series from a Haver Analytics Database into the active workfile. You must have a Haver Analytics database installed on your computer to use this feature. Syntax hfetch(database_name) series_name hfetch, if issued alone on a command line, will bring up a Haver dialog box which has fields for entering both the database name and the series names to be fetched. If you provide the database name (including the full path) in parentheses after the hfetch command, EViews will read the database and copy the series requested into the current workfile. It will also display information about the series on the status line. The database name is optional if a default database has been specified. hfetch can read multiple series with a single command. List the series names, each sepa- rated by a space. Examples hfetch(c:dataus1) pop gdp xyz reads the series POP, GDP, and XYZ from the US1 database into the current active workfile, performing frequency conversions if necessary. Cross-references See also Chapter 10. “EViews Databases,” on page 307 of User’s Guide I for a discussion of EViews and Haver databases. Additional information on EViews frequency conversion is provided in “Frequency Conversion” on page 160 of User’s Guide I. See also dbcreate (p. 342), db (p. 340), hconvert (p. 378) and hlabel (p. 380). hfetch Object Container, Data, and File Commands
- 387. 380—Chapter 12. Command Reference Histogram and descriptive statistics of a series. The hist command computes descriptive statistics and displays a histogram for the series. Syntax hist(options) series_name Options Examples hist lwage Displays the histogram and descriptive statistics of LWAGE. Cross-references See “Histogram and Stats” on page 376 of User’s Guide I for a discussion of the descriptive statistics reported in the histogram view. See distplot (p. 909) in the Object Reference for a more full-featured and customizable method of constructing histograms, and Series::hist (p. 568) in the Object Reference for the equivalent series object view command. Display a Haver Analytics database series description. hlabel reads the description of a series from a Haver Analytics Database and displays it on the status line at the bottom of the EViews window. Use this command to verify the contents of a Haver database series name. You must have a Haver Analytics database installed on your computer to use this feature. Syntax hlabel(database_name) series_name hlabel, if issued alone on a command line, will bring up a Haver dialog box which has fields for entering both the database name and the series names to be examined. If you pro- vide the database name in parentheses after the hlabel command, EViews will read the data- base and find the key information about the series in question, such as the start date, end hist Interactive Use Commands p Print the histogram. hlabel Object Container, Data, and File Commands
- 388. hpf—381 date, frequency, and description. This information is displayed in the status line at the bot- tom of the EViews window. Note that the database_name should refer to the full path to the Haver database but need not be specified if a default database has been specified in HAVERDIR.INI. If several series names are placed on the line, hlabel will gather the information about each of them, but the information display may scroll by so fast that only the last will be vis- ible. Examples hlabel(c:dataus1) pop displays the description of the series POP in the US1 database. Cross-references See Chapter 10. “EViews Databases,” on page 307 of User’s Guide I for a discussion of EViews and Haver databases. See also hfetch (p. 379) and hconvert (p. 378). Smooth a series using the Hodrick-Prescott filter. Syntax hpf(options) series_name filtered_name [@ cycle_name] Smoothing Options The degree of smoothing may be specified as an option. You may specify the smoothing as a value, or using a power rule: If no smoothing option is specified, EViews will use the power rule with a value of 2. hpf Interactive Use Commands lambda=arg Set smoothing parameter value to arg; a larger number results in greater smoothing. power=arg (default=2) Set smoothing parameter value using the frequency power rule of Ravn and Uhlig (2002) (the number of periods per year divided by 4, raised to the power arg, and multiplied by 1600). Hodrick and Prescott recommend the value 2; Ravn and Uhlig recommend the value 4.
- 389. 382—Chapter 12. Command Reference Other Options Examples hpf(lambda=1000) gdp gdp_hp smooths the GDP series with a smoothing parameter “1000” and saves the smoothed series as GDP_HP. Cross-references See “Hodrick-Prescott Filter” on page 493 of User’s Guide I for details. See Series::hpf (p. 569) of the Object Reference for the equivalent series object command. Imports data from a foreign file or a previously saved workfile into the current default workfile. The import command lets you perform four different types of data importing: dated reads, match-merge reads, sequential reads, and appends. Dated imports can only be performed if the destination workfile is a dated workfile. You must specify the date structure of the source data as part of the import command. EViews will then match the date structure of the source with that of the destination, and perform frequency conversion if necessary. Match-merge imports require both a source ID series and a destination ID series. EViews will read the source data into the destination workfile based upon matched values of the two ID series. Sequential imports will read the source data into the destination workfile by matching the first observation of the source file to the first observation in the destination workfile's cur- rent sample, then the second observation of the source with the second observation in the destination's current sample, and so on. Appended imports simply append the source data to the end of the destination workfile. Syntax The general form of the import command is: import([type=], options) source_description import_specification [@smpl smpl_string] [@genr genr_string] [@rename rename_string] prompt Force the dialog to appear from within a program. p Print the graph of the smoothed series and the original series. import Object Container, Data, and File Commands
- 390. import—383 where the syntax for import_specification depends on whether the read is a dated (“Dated Imports” on page 384), match-merge (“Match-Merge Import” on page 386), sequential (“Sequential Read” on page 388), or appended import (“Appended Read” on page 389). • Source_description should contain a description of the file from which the data is to be imported. The specification of the description is usually just the path and file name of the file, however you can also specify more precise information. See wfopen (p. 515) for more details on the specification of source_description. • The optional “type=” option may be used to specify a source type. For the most part, you should not need to specify a “type=” option as EViews will automatically deter- mine the type from the filename. The following table summaries the various source formats and along with the corresponding “type=” keywords: Option Keywords Access “access” Aremos-TSD “a”, “aremos”, “tsd” Binary “binary” dBASE “dbase” Excel (through 2003) “excel” Excel 2007 (xml) “excelxml” EViews Workfile --- Gauss Dataset “gauss” GiveWin/PcGive “g”, “give” HTML “html” Lotus 1-2-3 “lotus” ODBC Dsn File “dsn” ODBC Query File “msquery” ODBC Data Source “odbc” MicroTSP Workfile “dos”, “microtsp” MicroTSP Mac Workfile “mac” RATS 4.x “r”, “rats” RATS Portable / TROLL “l”, “trl” SAS Program “sasprog” SAS Transport “sasxport” SPSS “spss” SPSS Portable “spssport” Stata “stata”
- 391. 384—Chapter 12. Command Reference • The optional @smpl keyword may be used to specify that data is only imported for the observations specified by smpl_string. By default, the import will use all of the observations in the workfile. If @smpl is included, but no smpl_string is included, then the current default workfile sample is used. • The optional @genr keyword may be used to generate a new series in the workfile as part of the import procedure. genr_string may be any valid series creation expression, and can be an expression based upon one of the imported series. See genr (p. 365) for information on valid expressions. • The optional @rename keyword may be used to rename some of the imported series where rename_string contains pairs of old object names followed by the new names. See rename (p. 461) for additional discussion. In the remainder of this discussion, we examine each of the different import types in greater depth. Dated Imports The syntax for a dated import command is: import([type=], options) source_description @freq frequency start_date [@smpl smpl_string] [@genr genr_string] [@rename rename_string] The import_specification consists of the @freq keyword followed by a frequency argument specifying the frequency of the source data and a start_date to specify the starting date of the source data. See wfcreate (p. 510) for details on the forms that frequency may take. Text / ASCII “text” TSP Portable “t”, “tsp”
- 392. import—385 Basic Options Frequency Conversion Options The following options control the frequency conversion method when copying series and group objects into a workfile page and converting from low to high frequency: In addition, for Denton, Chow-Lin, and Litterman conversions, you must specify the indica- tor series by appending the keyword “@indicator” followed by the series name or names at the end of the import command. resize Extend the destination workfile (if necessary) to include the entire range of the source data. link Link the object to the source data so that the values can be refreshed at a later time. mode=arg (default=“o”) Set the behavior for handling name conflicts when an imported series already exists in the destination workfile. arg can be “o” (Completely replace existing series with source series. Note that values outside of the range of the source data will be overwritten with NAs), “u” (Overwrite existing series only for values within the range of the source data. Destination values outside of the source range will be unchanged), “ms” (Overwrite existing series, unless source value is an NA, in which case keep destination val- ues), “md” (Only overwrite NA values in destination series), “r” (rename any conflicts), or “p” (do not import any series which have a name conflict). page=page_name Optional name for the page into which the data should be imported. prompt Force the dialog to appear from within a program. c=arg Low to high conversion methods: “r” (constant match aver- age), “d” (constant match sum), “q” (quadratic match average), “t” (quadratic match sum), “i” (linear match last), “c” (cubic match last), “pointf” (point first), “pointl” (point last), “dentonf” (Denton first), “dentonl” (Denton last), “dentona” (Denton average), “dentons” (Denton sum), “chowlinf” (Chow-Lin first), “chowlinl” (Chow-Lin last), “chowlina” (Chow-Lin average),“chowlins” (Chow- Lin sum), “litmanf” (Litterman first), “litmanl” (Litterman last), “litmana” (Litterman average), “litmans” (Litterman sum).. rho=arg Autocorrelation coefficient (for Chow-Lin and Litterman conversions). Must be between 0 and 1, inclusive.
- 393. 386—Chapter 12. Command Reference When importing data from a higher frequency source into a lower frequency destination: Examples import c:tempquarterly.xls @freq q 1990 will import the file QUARTERLY.XLS into the current default workfile. The source file has a quarterly frequency, starting at 1990. import(c=s) c:tempquarterly.xls range="GDP_SHEET" @freq q 1990 @rename gdp_per_capita gdp will import from same file, but instead will use the data on the Excel sheet called “GDP_SHEET”, and will rename the series GDP_PER_CAPITA to GDP. A frequency conver- sion method using the sum of the nonmissing observations is used rather than the default average. import(mode=p) c:tempannual.txt @freq a 1990 @smpl 1994 1996 will import data from a text file called annual.txt, into the current default workfile. Any data in the text file that already exists in the destination workfile will be ignored, and for the remaining series, only the dates between 1994 and 1996 will be imported. Match-Merge Import Syntax import(options) source_description @id id @destid destid [@smpl smpl_string] [@genr genr_string] [@rename rename_string] The import_specification consists of the @id keyword and at least one ID series in the source file, followed by the @destid keyword and at least one ID series in the destination workfile. The two sets of ID series should be compatible, in that they should contain a sub- set of identical values that provide information on how observations from the two files should be matched. If one of the ID series is a date series, you should surround it with the @date( ) keyword. c=arg High to low conversion methods removing NAs: “a” (aver- age of the nonmissing observations), “s” (sum of the non- missing observations), “f” (first nonmissing observation), “l” (last nonmissing observation), “x” (maximum nonmiss- ing observation), “m” (minimum nonmissing observation). High to low conversion methods propagating NAs: “an” or “na” (average, propagating missings), “sn” or “ns” (sum, propagating missings), “fn” or “nf” (first, propagating missings), “ln” or “nl” (last, propagating missings), “xn” or “nx” (maximum, propagating missings), “mn” or “nm” (minimum, propagating missings).
- 394. import—387 Options resize Extend the destination workfile (if necessary) to include the entire range of the source data. link Link the object to the source data so that the values can be refreshed at a later time. mode=arg (default=“o”) Set the behavior for handling name conflicts when an imported series already exists in the destination workfile. arg can be “o” (Completely replace existing series with source series. Note that values outside of the range of the source data will be overwritten with NAs), “u” (Overwrite existing series only for values within the range of the source data. Destination values outside of the source range will be unchanged), “ms” (Overwrite existing series, unless source value is an NA, in which case keep destination val- ues), “md” (Only overwrite NA values in destination series), “r” (rename any conflicts), or “p” (do not import any series which have a name conflict). nacat Treat “NA” values as a category when copying using gen- eral match merge operations. propnas Propogate NAs / partial periods evaluate to NAs when con- verting. c=arg Set the match merge contraction method. If you are importing a numeric source series by general match merge, the argument can be one of: “mean”, “med” (median), “max”, “min”, “sum”, “sumsq” (sum-of squares), “var” (variance), “sd” (standard deviation), “skew” (skewness), “kurt” (kurtosis), “quant” (quantile, used with “quant=” option), “obs” (number of observa- tions), “nas” (number of NA values), “first” (first observa- tion in group), “last” (last observation in group), unique” (single unique group value, if present), “none” (disallow contractions). If importing an alpha series, only the non-summary meth- ods “max”, “min”, “obs”, “nas”, first”, “last”, “unique” and “none” are supported. For importing of numeric series, the default contraction method is “c=mean”; for copying of alpha series, the default is “c=unique”. page=page_name Optional name for the page into which the data should be imported. prompt Force the dialog to appear from within a program.
- 395. 388—Chapter 12. Command Reference Most of the conversion options should be self-explanatory. As for the others: “first” and “last” give the first and last non-missing observed for a given group ID; “obs” provides the number of non-missing values for a given group; “nas” reports the number of NAs in the group; “unique” will provide the value in the source series if it is the identical for all obser- vations in the group, and will return NA otherwise; “none” will cause the import to fail if there are multiple observations in any group—this setting may be used if you wish to pro- hibit all contractions. Examples import(c=max, type=excel) c:datastateunemp.xls @id states @destid states will import the file STATEUNEMP.XLS using the ID series STATES in both files as the match merge criteria. The maximum value is used as a contraction method. Note that although the “type=excel” option was used, it was not necessary since EViews will automatically detect the file type based on the file's extension (.xls). import c:datastategdp.txt colhead=3 delim=comma @id states @date(year) @destid states @date will import the file STATEGDP.TXT, specifying that there are three lines of column headers, and the delimiter for the text file is a comma. The series STATES is used as an ID series in both files, along with a date series (YEAR for the source file, and the default EViews date series, @DATE, for the destination workfile). Note that this type of import, with both a cross-section ID and a date ID is most commonly employed for importing data into panel workfiles. import c:datacagdp.xls @id states @date(year) @destid states @date @genr states="CA" will import the file CAGDP.XLS into the current workfile. In this particular case the source file is a time series file for a single state, California. Since the importing is being done into a panel workfile, the @genr keyword is used to generate a series containing the state identi- fier, CA, which is then used as the source ID. Sequential Read Syntax import(options) source_description [@smpl smpl_string] [@genr genr_string] [@rename rename_string] No import_specification is required for a sequential read.
- 396. import—389 Options Examples import(resize) sales.dta @smpl @all will import the Stata file SALES.DTA into the current workfile, using the entire workfile range. If the sales.dta file contains more observations that the current workfile, the current workfile is resized to accommodate the extra rows of data. Appended Read Syntax import(options) source_description @append [@genr genr_string] [@rename rename_string] The import_specification consists of the @append keyword. Note that the @smpl keyword is not supported for appended import. resize Extend the destination workfile (if necessary) to include the entire range of the source data. link Link the object to the source data so that the values can be refreshed at a later time. mode=arg (default=“o”) Set the behavior for handling name conflicts when an imported series already exists in the destination workfile. arg can be “o” (Completely replace existing series with source series. Note that values outside of the range of the source data will be overwritten with NAs), “u” (Overwrite existing series only for values within the range of the source data. Destination values outside of the source range will be unchanged), “ms” (Overwrite existing series, unless source value is an NA, in which case keep destination val- ues), “md” (Only overwrite NA values in destination series), “r” (rename any conflicts), or “p” (do not import any series which have a name conflict). page=page_name Optional name for the page into which the data should be imported. prompt Force the dialog to appear from within a program.
- 397. 390—Chapter 12. Command Reference Options Examples import(page=demand) demand.txt @append will append the text file, DEMAND.TXT, to the bottom of the page “demand” in the current workfile. Cross-references See Chapter 3. “Workfile Basics,” on page 41 of User’s Guide I for a discussion of workfiles. See also wfopen (p. 515), copy (p. 327), pageload (p. 440), read (p. 458), fetch (p. 355), wfsave (p. 529), and pagesave (p. 442). Imports observation values stored inside one or more series in a second workfile page into the attribute fields of objects within the current workfile page. Syntax importattr(options) source_page [@keep keeplist @drop droplist] The source page should contain one series whose values are the names of the objects into which the attributes should be imported, and one or more additional series containing the values of each attribute to be imported. By default, the procedure will assume there is an alpha series called NAME in the source page that contains the object names. The “name=” option can be used to specify that a different series in the source page contains the object names. link Link the object to the source data so that the values can be refreshed at a later time. mode=arg (default=“o”) Set the behavior for handling name conflicts when an imported series already exists in the destination workfile. arg can be “o” (Completely replace existing series with source series. Note that values outside of the range of the source data will be overwritten with NAs), “r” (rename any conflicts), or “p” (do not import any series which have a name conflict). page=page_name Optional name for the page into which the data should be imported. prompt Force the dialog to appear from within a program. importattr Object Container, Data, and File Commands
- 398. importattr—391 Values in the name column will always be converted into legal EViews names before match- ing to object names in the current workfile. (For example, any spaces will be replaced by underscores.) Typically, the name of the series in the source workfile will be used as the attribute name inside the current workfile. However, if an object in the source workfile has a display name set, this value will be used for the attribute name instead. Display names must be used to specify attribute names if your attribute names contain spaces since spaces are not allowed within EViews object names. For numeric series, attribute values will be imported using the formatting currently set for the series spreadsheet view of the source series. By default, all series in the source page will be used as attribute values (except the series containing object names). To import only a subset of series as attributes, name patterns can be provided in the @keep and @drop arguments to restrict which series will be used. importattr is most often useful when importing custom attributes from an external file. It is common for foreign data files, such as Excel files, to have one file (or sheet in Excel) con- taining the data, and a separate file (or sheet) containing the attributes, or meta-data, of each series. In such cases the pageload command can be used to read in the attribute file as a separate page in your workfile, and then importattr can be used to assign them to the data page. Options Examples importattr(name=objnames) attributes @keep attr1 attr2 mode=arg Specify how the procedure treats existing attribute val- ues in the current workfile page. arg may be "o" or "overwrite" (clears all existing attribute values in the object before applying the imported attribute value), “u” or “update” (clears existing attribute values only for the attributes that are being imported), “m” or “merge” (keeps existing values if the imported value of the attri- bute is empty), “md” (keeps all existing non-empty val- ues. only empty values will be replaced with the imported values). trim=string Remove string from the start and end of the attribute name. name=arg Specify the name of the alpha series in the source page containing the names of objects in the target page.
- 399. 392—Chapter 12. Command Reference Imports values from series ATTR1 and ATTR2 in the page “Attributes” into attributes “attr1” and “attr2” of objects in the current workfile. The series OBJNAMES in the page “Attributes” specifies which objects in the current workfile should have their attribute values updated. importattr(name=objnames, trim=":") attributes @keep attr1 attr2 removes the “:” from the start and end of the attribute name, if present, since EViews adds that character in the label view. Cross-references See “Workfile Details Display” on page 62 and “Label” on page 405 of User’s Guide I for a discussion of the workfile details display and, for example, the series object label view. Imports data from a foreign file into a matrix object in the current workfile. Syntax The general form of the importmat command is: importmat([type=], options) source_description import_specification • Source_description should contain a description of the file from which the data is to be imported. The specification of the description is usually just the path and file name of the file, however you can also specify more precise information. See wfopen (p. 515) for more details on the specification of source_description. • The optional “type=” option may be used to specify a source type. For the most part, you should not need to specify a “type=” option as EViews will automatically deter- mine the type from the filename. The following table summaries the various source formats and along with the corresponding “type=” keywords: importmat Object Container, Data, and File Commands Option Keywords Excel (through 2003) “excel” Excel 2007 (xml) “excelxml” HTML “html” Text / ASCII “text”
- 400. importmat—393 Options • import_specification can be used to provide additional information about the file to be read. The details of import_specification will depend upon the type of file being imported. Excel Files The syntax for reading Excel files is: importmat(options) source_description [table_description] [variables_description] The following table_description elements may be used when reading Excel data: • “range = arg”, where arg is a range of cells to read from the Excel workbook, follow- ing the standard Excel format [worksheet!][topleft_cell[:bottomright_cell]]. If the worksheet name contains spaces, it should be placed in single quotes. If the worksheet name is omitted, the cell range is assumed to refer to the currently active sheet. If only a top left cell is provided, a bottom right cell will be chosen automati- cally to cover the range of non-empty cells adjacent to the specified top left cell. If only a sheet name is provided, the first set of non-empty cells in the top left corner of the chosen worksheet will be selected automatically. As an alternative to specifying an explicit range, a name which has been defined inside the excel workbook to refer to a range or cell may be used to specify the cells to read. • “byrow”, transpose the incoming data. This option allows you to read files where the series are contained in rows (one row per series) rather than columns. The optional variables_description may be formed using the elements: • “colhead=int”, number of table rows to be treated as column headers. • “types=("arg1","arg2",…)”, user specified data types of the series. If types are pro- vided they will override the types automatically detected by EViews. You may use any of the following format keywords: “a” (character data), “f” (numeric data), “d” (dates), or “w”(EViews automatic detection). This option is rarely required. • “na="arg1"”, text used to represent observations that are missing from the file. The text should be enclosed on double quotes. name=arg Specify a name for the created matrix object. display=arg Specify a display name for the created matrix object. page=page_name Optional name for the page into which the matrix should be created. mode=o Overwrite an existing object with the same name (only applicable if the name option is used).
- 401. 394—Chapter 12. Command Reference • “scan=[int| all]”, number of rows of the table to scan during automatic format detec- tion (“scan=all” scans the entire file). • “firstobs=int”, first observation to be imported from the data (default is 1). This option may be used to start reading rows from partway through the table. • “lastobs = int”, last observation to be read from the data (default is last observation of the file). This option may be used to read only part of the file, which may be useful for testing. Excel Examples importmat "c:data filesdata.xls" loads the active sheet of DATA.XLSX into a new untitled matrix object. importmat(name=mymat) "c:data filesdata.xls" range="GDP data" reads the data contained in the “GDP data” sheet of “Data.XLS” into the MYMAT object. HTML Files The syntax for reading HTML pages is: importmat(options) source_description [table_description] [variables_description] The following table_description elements may be used when reading an HTML file or page: • “table = arg”, where arg specifies which HTML table to read in an HTML file/page containing multiple tables. When specifying arg, you should remember that tables are named automatically fol- lowing the pattern “Table01”, “Table02”, “Table03”, etc. If no table name is specified, the largest table found in the file will be chosen by default. Note that the table num- bering may include trivial tables that are part of the HTML content of the file, but would not normally be considered as data tables by a person viewing the page. • “skip = int”, where int is the number of rows to discard from the top of the HTML table. • “byrow”, transpose the incoming data. This option allows you to import files where the series are contained in rows (one row per series) rather than columns. The optional variables_description may be formed using the elements: • “colhead=int”, number of table rows to be treated as column headers. • “types=("arg1","arg2",…)”, user specified data types of the series. If types are pro- vided they will override the types automatically detected by EViews. You may use any of the following format keywords: “a” (character data), “f” (numeric data), “d” (dates), or “w”(EViews automatic detection). This option is rarely used.
- 402. importmat—395 • “na="arg1"”, text used to represent observations that are missing from the file. The text should be enclosed on double quotes. • “scan=[int|all]”, number of rows of the table to scan during automatic format detec- tion (“scan=all” scans the entire file). • “firstobs=int”, first observation to be imported from the table of data (default is 1). This option may be used to start reading rows from partway through the table. • “lastobs = int”, last observation to be read from the table of data (default is last observation of the file). This option may be used to read only part of the file, which may be useful for testing. HTML Examples importmat "c:data.html" loads into a new untitled matrix object the data located in the HTML file “Data.HTML” located on the C: drive importmat(type=html, name=forexmat) "http://www.tradingroom.com.au/apps/mkt/forex.ac" colhead=3 loads into a matrix object called FOREXMAT the data with the given URL located on the website site “http://www.tradingroom.com.au”. The column header is set to three rows. Text and Binary Files The syntax for reading text or binary files is: importmat(options) source_description [table_description] [variables_description] If a table_description is not provided, EViews will attempt to read the file as a free-format text file. The following table_description elements may be used when reading a text or binary file: • “ftype = [ascii|binary]” specifies whether numbers and dates in the file are stored in a human readable text (ASCII), or machine readable (Binary) form. • “rectype = [crlf|fixed|streamed]” describes the record structure of the file: “crlf”, each row in the output table is formed using a fixed number of lines from the file (where lines are separated by carriage return/line feed sequences). This is the default setting. “fixed”, each row in the output table is formed using a fixed number of charac- ters from the file (specified in “reclen= arg”). This setting is typically used for files that contain no line breaks. “streamed”, each row in the output table is formed by reading a fixed number of fields, skipping across lines if necessary. This option is typically used for files that contain line breaks, but where the line breaks are not relevant to how rows from the data should be formed.
- 403. 396—Chapter 12. Command Reference • “reclines =int”, number of lines to use in forming each row when “rectype=crlf” (default is 1). • “reclen=int”, number of bytes to use in forming each row when “rectype=fixed”. • “recfields=int”, number of fields to use in forming each row when “rec- type=streamed”. • “skip=int”, number of lines (if rectype is “crlf”) or bytes (if rectype is not “crlf”) to discard from the top of the file. • “comment=string“, where string is a double-quoted string, specifies one or more characters to treat as a comment indicator. When a comment indicator is found, everything on the line to the right of where the comment indicator starts is ignored. • “emptylines=[keep|drop]”, specifies whether empty lines should be ignored (“drop”), or treated as valid lines (“keep”) containing missing values. The default is to ignore empty lines. • “tabwidth=int”, specifies the number of characters between tab stops when tabs are being replaced by spaces (default=8). Note that tabs are automatically replaced by spaces whenever they are not being treated as a field delimiter. • “fieldtype=[delim|fixed|streamed|undivided]”, specifies the structure of fields within a record: “Delim”, fields are separated by one or more delimiter characters “Fixed”, each field is a fixed number of characters “Streamed”, fields are read from left to right, with each field starting immediately after the previous field ends. “Undivided”, read entire record as a single series. • “quotes=[single|double|both|none]”, specifies the character used for quoting fields, where “single” is the apostrophe, “double” is the double quote character, and “both” means that either single or double quotes are allowed (default is “both”). Characters contained within quotes are never treated as delimiters. • “singlequote“, same as “quotes = single”. • “delim=[comma|tab|space|dblspace|white|dblwhite]”, specifies the character(s) to treat as a delimiter. “White” means that either a tab or a space is a valid delimiter. You may also use the abbreviation “d=” in place of “delim=”. • “custom="arg1"”, specifies custom delimiter characters in the double quoted string. Use the character “t” for tab, “s” for space and “a” for any character. • “mult=[on|off]”, to treat multiple delimiters as one. Default value is “on” if “delim” is “space”, “dblspace”, “white”, or “dblwhite”, and “off” otherwise.
- 404. importmat—397 • “endian = [big|little]”, selects the endianness of numeric fields contained in binary files. • “string = [nullterm|nullpad|spacepad]”, specifies how strings are stored in binary files. If “nullterm”, strings shorter than the field width are terminated with a single zero character. If “nullpad”, strings shorter than the field width are followed by extra zero characters up to the field width. If “spacepad”, strings shorter than the field width are followed by extra space characters up to the field width. • “byrow”, transpose the incoming data. This option allows you to import files where the series are contained in rows (one row per series) rather than columns. A central component of the table_description element is the format statement. You may specify the data format using the following table descriptors: • Fortran Format: fformat=([n1]Type[Width][.Precision], [n2]Type[Width][.Precision], ...) where Type specifies the underlying data type, and may be one of the following, I - integer F - fixed precision E - scientific A - alphanumeric X - skip and n1, n2, ... are the number of times to read using the descriptor (default=1). More complicated Fortran compatible variations on this format are possible. • Column Range Format: rformat="[n1]Type[Width][.Precision], [n2]Type[Width][.Precision], ...)" where optional type is “$” for string or “#” for number, and n1, n2, n3, n4, etc. are the range of columns containing the data. • C printf/scanf Format: cformat="fmt" where fmt follows standard C language (printf/scanf) format rules. The optional variables_description may be formed using the elements: • “colhead=int”, number of table rows to be treated as column headers. • “types=("arg1","arg2",…)”, user specified data types of the series. If types are pro- vided they will override the types automatically detected by EViews. You may use any of the following format keywords: “a” (character data), “f” (numeric data), “d” (dates), or “w”(EViews automatic detection). This option is rarely used.
- 405. 398—Chapter 12. Command Reference • “na="arg1"”, text used to represent observations that are missing from the file. The text should be enclosed on double quotes. • “scan=[int|all]”, number of rows of the table to scan during automatic format detec- tion (“scan=all” scans the entire file). • “firstobs=int”, first observation to be imported from the table of data (default is 1). This option may be used to start reading rows from partway through the table. • “lastobs = int”, last observation to be read from the table of data (default is last observation of the file). This option may be used to read only part of the file, which may be useful for testing. Text and Binary File Examples (.txt, .csv, etc.) importmat c:data.csv skip=5 reads “Data.CSV” into a new unnamed matrix object, skipping the first 5 rows. importtbl(type=text, name=matrix01) c:date.txt delim=comma loads the comma delimited data DATE.TXT into the MATRIX01 matrix object. Cross-references See Chapter 3. “Workfile Basics,” on page 41 of User’s Guide I for a discussion of workfiles. See also wfopen (p. 515), copy (p. 327), pageload (p. 440), read (p. 458), fetch (p. 355), wfsave (p. 529), and pagesave (p. 442). Imports data from a foreign file into a table object in the current workfile. Syntax The general form of the importtbl command is: importtbl([type=], options) source_description import_specification • Source_description should contain a description of the file from which the data is to be imported. The specification of the description is usually just the path and file name of the file, however you can also specify more precise information. See wfopen (p. 515) for more details on the specification of source_description. • The optional “type=” option may be used to specify a source type. For the most part, you should not need to specify a “type=” option as EViews will automatically deter- mine the type from the filename. The following table summaries the various source formats and along with the corresponding “type=” keywords: importtbl Object Container, Data, and File Commands
- 406. importtbl—399 Options • import_specification can be used to provide additional information about the file to be read. The details of import_specification will depend upon the type of file being imported. Excel Files The syntax for reading Excel files is: importtbl(options) source_description [table_description] [variables_description] The following table_description elements may be used when reading Excel data: • “range = arg”, where arg is a range of cells to read from the Excel workbook, follow- ing the standard Excel format [worksheet!][topleft_cell[:bottomright_cell]]. If the worksheet name contains spaces, it should be placed in single quotes. If the worksheet name is omitted, the cell range is assumed to refer to the currently active sheet. If only a top left cell is provided, a bottom right cell will be chosen automati- cally to cover the range of non-empty cells adjacent to the specified top left cell. If only a sheet name is provided, the first set of non-empty cells in the top left corner of the chosen worksheet will be selected automatically. As an alternative to specifying an explicit range, a name which has been defined inside the excel workbook to refer to a range or cell may be used to specify the cells to read. • “byrow”, transpose the incoming data. This option allows you to read files where the series are contained in rows (one row per series) rather than columns. The optional variables_description may be formed using the elements: • “colhead=int”, number of table rows to be treated as column headers. Option Keywords Excel (through 2003) “excel” Excel 2007 (xml) “excelxml” HTML “html” Text / ASCII “text” name=arg Specify a name for the created table object. display=arg Specify a display name for the created table object. page=page_name Optional name for the page into which the table should be created. mode=o Overwrite an existing object with the same name (only applicable if the name option is used).
- 407. 400—Chapter 12. Command Reference • “types=("arg1","arg2",…)”, user specified data types of the series. If types are pro- vided they will override the types automatically detected by EViews. You may use any of the following format keywords: “a” (character data), “f” (numeric data), “d” (dates), or “w”(EViews automatic detection). This option is rarely required. • “na="arg1"”, text used to represent observations that are missing from the file. The text should be enclosed on double quotes. • “scan=[int| all]”, number of rows of the table to scan during automatic format detec- tion (“scan=all” scans the entire file). • “firstobs=int”, first observation to be imported from the data (default is 1). This option may be used to start reading rows from partway through the table. • “lastobs = int”, last observation to be read from the data (default is last observation of the file). This option may be used to read only part of the file, which may be useful for testing. Excel Examples importtbl "c:data filesdata.xls" loads the active sheet of DATA.XLSX into a new untitled table object. importtbl(name=mytbl) "c:data filesdata.xls" range="GDP data" reads the data contained in the “GDP data” sheet of “Data.XLS” into the MYTBL object. HTML Files The syntax for reading HTML pages is: importtbl(options) source_description [table_description] [variables_description] The following table_description elements may be used when reading an HTML file or page: • “table = arg”, where arg specifies which HTML table to read in an HTML file/page containing multiple tables. When specifying arg, you should remember that tables are named automatically fol- lowing the pattern “Table01”, “Table02”, “Table03”, etc. If no table name is specified, the largest table found in the file will be chosen by default. Note that the table num- bering may include trivial tables that are part of the HTML content of the file, but would not normally be considered as data tables by a person viewing the page. • “skip = int”, where int is the number of rows to discard from the top of the HTML table. • “byrow”, transpose the incoming data. This option allows you to import files where the series are contained in rows (one row per series) rather than columns. The optional variables_description may be formed using the elements:
- 408. importtbl—401 • “colhead=int”, number of table rows to be treated as column headers. • “types=("arg1","arg2",…)”, user specified data types of the series. If types are pro- vided they will override the types automatically detected by EViews. You may use any of the following format keywords: “a” (character data), “f” (numeric data), “d” (dates), or “w”(EViews automatic detection). This option is rarely used. • “na="arg1"”, text used to represent observations that are missing from the file. The text should be enclosed on double quotes. • “scan=[int|all]”, number of rows of the table to scan during automatic format detec- tion (“scan=all” scans the entire file). • “firstobs=int”, first observation to be imported from the table of data (default is 1). This option may be used to start reading rows from partway through the table. • “lastobs = int”, last observation to be read from the table of data (default is last observation of the file). This option may be used to read only part of the file, which may be useful for testing. HTML Examples importtbl "c:data.html" loads into a new untitled table object the data located in the HTML file “Data.HTML” located on the C: drive importtbl(type=html, name=forextbl) "http://www.tradingroom.com.au/apps/mkt/forex.ac" colhead=3 loads into a table object called FOREXTBL the data with the given URL located on the web- site site “http://www.tradingroom.com.au”. The column header is set to three rows. Text and Binary Files The syntax for reading text or binary files is: importtbl(options) source_description [table_description] [variables_description] If a table_description is not provided, EViews will attempt to read the file as a free-format text file. The following table_description elements may be used when reading a text or binary file: • “ftype = [ascii|binary]” specifies whether numbers and dates in the file are stored in a human readable text (ASCII), or machine readable (Binary) form. • “rectype = [crlf|fixed|streamed]” describes the record structure of the file: “crlf”, each row in the output table is formed using a fixed number of lines from the file (where lines are separated by carriage return/line feed sequences). This is the default setting. “fixed”, each row in the output table is formed using a fixed number of charac-
- 409. 402—Chapter 12. Command Reference ters from the file (specified in “reclen= arg”). This setting is typically used for files that contain no line breaks. “streamed”, each row in the output table is formed by reading a fixed number of fields, skipping across lines if necessary. This option is typically used for files that contain line breaks, but where the line breaks are not relevant to how rows from the data should be formed. • “reclines =int”, number of lines to use in forming each row when “rectype=crlf” (default is 1). • “reclen=int”, number of bytes to use in forming each row when “rectype=fixed”. • “recfields=int”, number of fields to use in forming each row when “rec- type=streamed”. • “skip=int”, number of lines (if rectype is “crlf”) or bytes (if rectype is not “crlf”) to discard from the top of the file. • “comment=string“, where string is a double-quoted string, specifies one or more characters to treat as a comment indicator. When a comment indicator is found, everything on the line to the right of where the comment indicator starts is ignored. • “emptylines=[keep|drop]”, specifies whether empty lines should be ignored (“drop”), or treated as valid lines (“keep”) containing missing values. The default is to ignore empty lines. • “tabwidth=int”, specifies the number of characters between tab stops when tabs are being replaced by spaces (default=8). Note that tabs are automatically replaced by spaces whenever they are not being treated as a field delimiter. • “fieldtype=[delim|fixed|streamed|undivided]”, specifies the structure of fields within a record: “Delim”, fields are separated by one or more delimiter characters “Fixed”, each field is a fixed number of characters “Streamed”, fields are read from left to right, with each field starting immediately after the previous field ends. “Undivided”, read entire record as a single series. • “quotes=[single|double|both|none]”, specifies the character used for quoting fields, where “single” is the apostrophe, “double” is the double quote character, and “both” means that either single or double quotes are allowed (default is “both”). Characters contained within quotes are never treated as delimiters. • “singlequote“, same as “quotes = single”.
- 410. importtbl—403 • “delim=[comma|tab|space|dblspace|white|dblwhite]”, specifies the character(s) to treat as a delimiter. “White” means that either a tab or a space is a valid delimiter. You may also use the abbreviation “d=” in place of “delim=”. • “custom="arg1"”, specifies custom delimiter characters in the double quoted string. Use the character “t” for tab, “s” for space and “a” for any character. • “mult=[on|off]”, to treat multiple delimiters as one. Default value is “on” if “delim” is “space”, “dblspace”, “white”, or “dblwhite”, and “off” otherwise. • “endian = [big|little]”, selects the endianness of numeric fields contained in binary files. • “string = [nullterm|nullpad|spacepad]”, specifies how strings are stored in binary files. If “nullterm”, strings shorter than the field width are terminated with a single zero character. If “nullpad”, strings shorter than the field width are followed by extra zero characters up to the field width. If “spacepad”, strings shorter than the field width are followed by extra space characters up to the field width. • “byrow”, transpose the incoming data. This option allows you to import files where the series are contained in rows (one row per series) rather than columns. A central component of the table_description element is the format statement. You may specify the data format using the following table descriptors: • Fortran Format: fformat=([n1]Type[Width][.Precision], [n2]Type[Width][.Precision], ...) where Type specifies the underlying data type, and may be one of the following, I - integer F - fixed precision E - scientific A - alphanumeric X - skip and n1, n2, ... are the number of times to read using the descriptor (default=1). More complicated Fortran compatible variations on this format are possible. • Column Range Format: rformat="[n1]Type[Width][.Precision], [n2]Type[Width][.Precision], ...)" where optional type is “$” for string or “#” for number, and n1, n2, n3, n4, etc. are the range of columns containing the data. • C printf/scanf Format: cformat="fmt" where fmt follows standard C language (printf/scanf) format rules.
- 411. 404—Chapter 12. Command Reference The optional variables_description may be formed using the elements: • “colhead=int”, number of table rows to be treated as column headers. • “types=("arg1","arg2",…)”, user specified data types of the series. If types are pro- vided they will override the types automatically detected by EViews. You may use any of the following format keywords: “a” (character data), “f” (numeric data), “d” (dates), or “w”(EViews automatic detection). This option is rarely used. • “na="arg1"”, text used to represent observations that are missing from the file. The text should be enclosed on double quotes. • “scan=[int|all]”, number of rows of the table to scan during automatic format detec- tion (“scan=all” scans the entire file). • “firstobs=int”, first observation to be imported from the table of data (default is 1). This option may be used to start reading rows from partway through the table. • “lastobs = int”, last observation to be read from the table of data (default is last observation of the file). This option may be used to read only part of the file, which may be useful for testing. Text and Binary File Examples (.txt, .csv, etc.) importtbl c:data.csv skip=5 reads “Data.CSV” into a new unnamed table object, skipping the first 5 rows. importtbl(type=text, name=table01) c:date.txt delim=comma loads the comma delimited data DATE.TXT into the TABLE01 table object. Cross-references See Chapter 3. “Workfile Basics,” on page 41 of User’s Guide I for a discussion of workfiles. See also wfopen (p. 515), copy (p. 327), pageload (p. 440), read (p. 458), fetch (p. 355), wfsave (p. 529), and pagesave (p. 442). Limited Information Maximum Likelihood and K-class Estimation. Syntax liml(options) y c x1 [x2 x3 ...] @ z1 [z2 z3 ...] liml(options) specification @ z1 [z2 z3 ...] To use the liml command, list the dependent variable first, followed by the regressors, then any AR or MA error specifications, then an “@”-sign, and finally, a list of exogenous instru- ments. liml Interactive Use Commands
- 412. liml—405 You may estimate nonlinear equations or equations specified with formulas by first provid- ing a specification, then listing the instrumental variables after an “@”-sign. There must be at least as many instrumental variables as there are independent variables. All exogenous variables included in the regressor list should also be included in the instrument list. A con- stant is included in the list of instrumental variables, unless the noconst option is specified. Options noconst Do not include a constant in the instrumental list. Without this option, a constant will always be included as an instru- ment, even if not specified explicitly. w=arg Weight series or expression. wtype=arg (default=“istdev”) Weight specification type: inverse standard deviation (“istdev”), inverse variance (“ivar”), standard deviation (“stdev”), variance (“var”). wscale=arg Weight scaling: EViews default (“eviews”), average (“avg”), none (“none”). The default setting depends upon the weight type: “eviews” if “wtype=istdev”, “avg” for all others. kclass=number Set the value of in the K-class estimator. If omitted, LIML is performed, and is calculated as part of the estimation procedure. se = arg (default=“iv”) Set the standard-error calculation type: IV based (“se=iv”), K-Class based (“se=kclass”), Bekker (“se=bekk”), or Hansen, Hausman, and Newey (“se=hhn”). m=integer Set maximum number of iterations. c=number Set convergence criterion. The criterion is based upon the maximum of the percentage changes in the scaled coeffi- cients. The criterion will be set to the nearest value between 1e-24 and 0.2. numericderiv / -numericderiv [Do / do not] use numeric derivatives only. If omitted, EViews will follow the global default. fastderiv / -fastderiv [Do / do not] use fast derivative computation. If omitted, EViews will follow the global default. Available only for legacy estimation (“optmeth=legacy”). showopts / -showopts [Do / do not] display the starting coefficient values and estimation options in the estimation output. k k
- 413. 406—Chapter 12. Command Reference Examples liml gdp c cpi inc @ lw lw(-1) calculates a LIML estimation of GDP on a constant, CPI, and INC, using a constant, LW, and LW(-1) as instruments. Cross-references See “Limited Information Maximum Likelihood and K-Class Estimation” on page 65 of User’s Guide II for discussion. See Equation::liml (p. 113) for the equivalent equation object method. Load a workfile. Provided for backward compatibility. Same as wfopen (p. 515). Clears the log window corresponding to the program. Use this command anytime from within a program to clear the log of the program. Syntax logclear Cross-references See “Program Message Logging” on page 135 for details. See also logmode (p. 407), logmsg (p. 409), and logsave (p. 409). coef=arg Specify the name of the coefficient vector (if specified by list); the default behavior is to use the “C” coefficient vec- tor. prompt Force the dialog to appear from within a program. p Print estimation results. load Object Container, Data, and File Commands logclear Programming Commands
- 414. logmode—407 Estimate binary models with logistic errors. Provide for backward compatibility. Equivalent to issuing the command, binary with the option “(d=l)”. See binary (p. 299). Set the log settings of specified message types. Activates or deactivates the logging of specified message types if message types are set to be program controlled. Syntax logmode msgtype_list where msgtype_list is a list of the message types to update. Options Message type options logit Interactive Use Commands logmode Programming Commands all/-all [Show/Do not show] all messages. error/-error, e/-e [Show/Do not show] error messages. logmsg /-logmsg, l/ -l [Show/Do not show] logmsg messages. program/-program, p/-p [Show/Do not show] program lines. statusline/-sta- tusline, s / -s [Show/Do not show] status line messages.
- 415. 408—Chapter 12. Command Reference Note that using logmode with debug will override all subsequent (in either the current pro- gram or any program run using exec or run), logmode with hideprog and -error specifi- cations. In particular, debug will override subsequent logmode addin statements. Examples logmode p turns on logging of program lines of code. Note that by default all message types are initially turned off. logmode error -p Building on the first command, this activates the logging of errors and deactivates the log- ging of program lines. logmode -all s turns off logging of all types, with exception to status line messages. Note the order of mes- sage types is important. For example, logmode p -all will initially activate the logging of program lines, but following p with -all will deactivate program lines as well as any other messages that have been previously activated. Cross-references See “Program Message Logging” on page 135 for details. See also “Program Modes” on page 134. See also logclear (p. 406), logmsg (p. 409), and logsave (p. 409). hideprogline/- hideprogline [Hide/Do not hide] the program line when reporting errors encountered during execution. addin/-addin [Show/Do not show] messages generally appropriate for addin error reporting. addin is equivalent to the command and program mode (“Program Modes” on page 134) state- ments: logmode hideprogline -error mode quiet debug Show messages generally appropriate for debugging of pro- grams. Equivalent to the command: logmode -hideprogline error
- 416. logsave—409 Adds a line of text to the program log. Syntax logmsg text Example logmsg About to iterate through list of states appends the text “About to iterate through list of states” to the program log. Cross-references See “Program Message Logging” on page 135 for details. See also logclear (p. 406), logmode (p. 407), and logsave (p. 409). Saves the program log to a text file. Syntax logsave filepath where filepath is the location and filename for saving the log file. Options Example logsave c:EViewsmyprog.text saves the contents of the program log to the text file MYPROG, in the “C:EViews” directory. Cross-references See “Program Message Logging” on page 135 for details. See also logclear (p. 406), logmode (p. 407), and logmsg (p. 409). logmsg Programming Commands logsave Programming Commands type=rtf Save the log as an RTF file (thus preserving syntax color- ing).
- 417. 410—Chapter 12. Command Reference Estimation by linear or nonlinear least squares regression. When the current workfile has a panel structure, ls also estimates cross-section weighed least squares, feasible GLS, and fixed and random effects models. Syntax ls(options) y x1 [x2 x3 ...] ls(options) specification For linear specifications, list the dependent variable first, followed by a list of the indepen- dent variables. Use a “C” if you wish to include a constant or intercept term; unlike some programs, EViews does not automatically include a constant in the regression. You may add AR, MA, SAR, and SMA error specifications, a D fractional differencing term, and PDL spec- ifications for polynomial distributed lags. If you include lagged variables, EViews will adjust the sample automatically, if necessary. Both dependent and independent variables may be created from existing series using stan- dard EViews functions and transformations. EViews treats the equation as linear in each of the variables and assigns coefficients C(1), C(2), and so forth to each variable in the list. Linear or nonlinear single equations may also be specified by explicit equation. You should specify the equation as a formula. The parameters to be estimated should be included explicitly: “C(1)”, “C(2)”, and so forth (assuming that you wish to use the default coefficient vector “C”). You may also declare an alternative coefficient vector using coef and use these coefficients in your expressions. Options Non-Panel LS Options ls Interactive Use Commands w=arg Weight series or expression. Note: we recommend that, absent a good reason, you employ the default settings Inverse std. dev. weights (“wtype=istdev”) with EViews default scaling (“wscale=eviews”) for backward compatibility with ver- sions prior to EViews 7. wtype=arg (default=“istdev”) Weight specification type: inverse standard deviation (“ist- dev”), inverse variance (“ivar”), standard deviation (“stdev”), variance (“var”).
- 418. ls—411 wscale=arg Weight scaling: EViews default (“eviews”), average (“avg”), none (“none”). The default setting depends upon the weight type: “eviews” if “wtype=istdev”, “avg” for all others. z Turn off backcasting in ARMA models where “arma=cls”. optmethod = arg Optimization method for nonlinear least squares and ARMA: “bfgs” (BFGS); “newton” (Newton-Raphson), “opg” or “bhhh” (OPG or BHHH), “kohn” (Kohn-Ansley for ARMA estimated by ML or GLS), or “legacy” (EViews legacy for nonlinear least squares and ARMA estimated by CLS). Gauss-Newton is the default method. optstep = arg Step method for nonlinear least squares and ARMA: “mar- quardt” (Marquardt); “dogleg” (Dogleg); “linesearch” (Line search). Marquardt is the default method. cov=arg Covariance method: “ordinary” (default method based on inverse of the estimated information matrix), “huber” or “white” (Huber-White sandwich method available for non- linear least squares or ARMA estimated by CLS), “hac” (Newey-West HAC, available for nonlinear least squares or ARMA estimated by CLS).. covinfo = arg Information matrix method: “opg” (OPG); “hessian” (observed Hessian). (Applicable when non-legacy “optmethod=”.) nodf Do not perform degree of freedom corrections in computing coefficient covariance matrix. The default is to use degree of freedom corrections. covlag=arg (default=1) Whitening lag specification: integer (user-specified lag value), “a” (automatic selection). covinfosel=arg (default=“aic”) Information criterion for automatic selection: “aic” (Akaike), “sic” (Schwarz), “hqc” (Hannan-Quinn) (if “lag=a”). covmaxlag=integer Maximum lag-length for automatic selection (optional) (if “lag=a”). The default is an observation-based maximum of .T 1 3
- 419. 412—Chapter 12. Command Reference covkern=arg (default=“bart”) Kernel shape: “none” (no kernel), “bart” (Bartlett, default), “bohman” (Bohman), “daniell” (Daniel), “parzen” (Parzen), “parzriesz” (Parzen-Riesz), “parzgeo” (Parzen- Geometric), “parzcauchy” (Parzen-Cauchy), “quadspec” (Quadratic Spectral), “trunc” (Truncated), “thamm” (Tukey-Hamming), “thann” (Tukey-Hanning), “tparz” (Tukey-Parzen). covbw=arg (default=“fixednw” ) Kernel Bandwidth: “fixednw” (Newey-West fixed), “andrews” (Andrews automatic), “neweywest” (Newey- West automatic), number (User-specified bandwidth). covnwlag=integer Newey-West lag-selection parameter for use in nonpara- metric kernel bandwidth selection (if “covbw=newey- west”). covbwint Use integer portion of bandwidth. m=integer Set maximum number of iterations. c=scalar Set convergence criterion. The criterion is based upon the maximum of the percentage changes in the scaled coeffi- cients. The criterion will be set to the nearest value between 1e-24 and 0.2. arma=arg ARMA estimation method: “ml” (maximum likelihood); “gls” (generalized least squares), “cls” (conditional least squares). Not applicable to ARFIMA models which always estimate using maximum likelihood. armastart=arg ARMA coefficient starting values: “auto” (automatic) “fixed” (legacy EViews fixed); “random” (random draw); “user” (user-specified). Applicable when “arma=ml” or “arma=gls”. s Use the current coefficient values in estimator coefficient vector as starting values for equations specified by list with AR or MA terms when “arma=cls” (see also param (p. 453) of the Command and Programming Reference). s=number Determine starting values for equations specified by list with AR or MA terms when “arma=cls”. Specify a number between zero and one representing the fraction of prelimi- nary least squares estimates computed without AR or MA terms to be used. Note that out of range values are set to “s=1”. Specifying “s=0” initializes coefficients to zero. By default EViews uses “s=1”. Does not apply to coefficients for AR and MA terms which are set to EViews determined default values.
- 420. ls—413 Note: not all options are available for all equation methods. See the User’s Guide II for details on each estimation method. Panel LS Options numericderiv / -numericderiv [Do / do not] use numeric derivatives only. If omitted, EViews will follow the global default. fastderiv / -fastderiv [Do / do not] use fast derivative computation. If omitted, EViews will follow the global default. Available only for legacy estimation (“optmeth=legacy”). showopts / -showopts [Do / do not] display the starting coefficient values and estimation options in the estimation output. coef=arg Specify the name of the coefficient vector (if specified by list); the default behavior is to use the “C” coefficient vec- tor. prompt Force the dialog to appear from within a program. p Print estimation results. cx=arg Cross-section effects: (default) none, fixed effects (“cx=f”), random effects (“cx=r”). per=arg Period effects: (default) none, fixed effects (“per=f”), ran- dom effects (“per=r”). wgt=arg GLS weighting: (default) none, cross-section system weights (“wgt=cxsur”), period system weights (“wgt=persur”), cross-section diagonal weighs (“wgt=cxdiag”), period diagonal weights (“wgt=per- diag”). cov=arg Coefficient covariance method: (default) ordinary, White cross-section system robust (“cov=cxwhite”), White period system robust (“cov=perwhite”), White heteroske- dasticity robust (“cov=stackedwhite”), Cross-section sys- tem robust/PCSE (“cov=cxsur”), Period system robust/PCSE (“cov=persur”), Cross-section heteroskedas- ticity robust/PCSE (“cov=cxdiag”), Period heteroskedastic- ity robust/PCSE (“cov=perdiag”). keepwgts Keep full set of GLS weights used in estimation with object, if applicable (by default, only small memory weights are saved). rancalc=arg (default=“sa”) Random component method: Swamy-Arora (“ran- calc=sa”), Wansbeek-Kapteyn (“rancalc=wk”), Wallace- Hussain (“rancalc=wh”).
- 421. 414—Chapter 12. Command Reference nodf Do not perform degree of freedom corrections in computing coefficient covariance matrix. The default is to use degree of freedom corrections. coef=arg Specify the name of the coefficient vector (if specified by list); the default behavior is to use the “C” coefficient vec- tor. iter=arg (default= “onec”) Iteration control for GLS specifications: perform one weight iteration, then iterate coefficients to convergence (“iter=onec”), iterate weights and coefficients simultane- ously to convergence (“iter=sim”), iterate weights and coefficients sequentially to convergence (“iter=seq”), per- form one weight iteration, then one coefficient step (“iter=oneb”). Note that random effects models currently do not permit weight iteration to convergence. unbalsur Compute SUR factorization in unbalanced data using the subset of available observations for a cluster. m=integer Set maximum number of iterations. c=scalar Set convergence criterion. The criterion is based upon the maximum of the percentage changes in the scaled coeffi- cients. The criterion will be set to the nearest value between 1e-24 and 0.2. s Use the current coefficient values in estimator coefficient vector as starting values for equations specified by list with AR terms (see also param (p. 453) of the Command and Programming Reference). s=number Determine starting values for equations specified by list with AR terms. Specify a number between zero and one representing the fraction of preliminary least squares esti- mates computed without AR terms to be used. Note that out of range values are set to “s=1”. Specifying “s=0” ini- tializes coefficients to zero. By default EViews uses “s=1”. Does not apply to coefficients for AR terms which are instead set to EViews determined default values. numericderiv / -numericderiv [Do / do not] use numeric derivatives only. If omitted, EViews will follow the global default. fastderiv / -fastderiv [Do / do not] use fast derivative computation. If omitted, EViews will follow the global default. showopts / -showopts [Do / do not] display the starting coefficient values and estimation options in the estimation output.
- 422. ls—415 Examples ls m1 c uemp inf(0 to -4) @trend(1960:1) estimates a linear regression of M1 on a constant, UEMP, INF (from current up to four lags), and a linear trend. ls(z) d(tbill) c inf @seas(1) @seas(1)*inf ma(2) regresses the first difference of TBILL on a constant, INF, a seasonal dummy, and an interac- tion of the dummy and INF, with an MA(2) error. The “z” option turns off backcasting. coef(2) beta param beta(1) .2 beta(2) .5 c(1) 0.1 ls(cov=white) q = beta(1)+beta(2)*(l^c(1) + k^(1-c(1))) estimates the nonlinear regression starting from the specified initial values. The “cov=white” option reports heteroskedasticity consistent standard errors. ls r = c(1)+c(2)*r(-1)+div(-1)^c(3) estimates an UNTITLED nonlinear equation. ls(cx=f, per=f) n w k ys c estimates an UNTITLED equation in a panel workfile using both cross-section and period fixed effects. ls(cx=f, wgt=cxdiag) n w k ys c estimates an UNTITLED equation in a panel workfile with cross-section weights and fixed effects. Cross-references Chapter 19. “Basic Regression Analysis,” on page 5 and Chapter 20. “Additional Regression Tools,” on page 23 of User’s Guide II discuss the various regression methods in greater depth. Chapter 16. “Special Expression Reference,” on page 601 describes special terms that may be used in ls specifications. See Chapter 17. “Panel Estimation,” on page 855 of User’s Guide II for a discussion of panel equation estimation. See Equation::ls (p. 115) for the equivalent equation object method command. prompt Force the dialog to appear from within a program. p Print basic estimation results.
- 423. 416—Chapter 12. Command Reference Estimates an equation using Mixed Data Sampling (MIDAS) regression. MIDAS regression is an estimation technique which allows for data sampled at different fre- quencies to be used in the same regression. Syntax midas(options) y x1 [x2 x3 ...] @ z1pagez1 [z2pagez2 ...] where y, x1, etc., are the dependent and explanatory variables in the current page frequency, and z1pagez1 and z2pagez2 are the high frequency variable pageseries specification. You may not include ARMA terms in a MIDAS regression. Options midas Interactive Use Commands midwgt=arg MIDAS weight method: step function(“step”), normalized exponential Almon (“expalmon”), normalized beta func- tion (“beta”), or the default Almon/PDL weighting (“almon”). lag=arg Method for specifying the number of lags of the high fre- quency regressor to use: lag selection (“auto”), fixed (“fixed”). The default is “lag=fixed”. maxlag=arg Maximum number of lags of the high frequency regressor to use when using lag selection. For use when “lag=auto”. The default value is 4. fixedlag=arg Fixed number of lags of the high frequency regressor to use. For use when “lags=fixed”. The default value is 4. steps=integer Stepsize (number of high frequency periods to group). For use when “midwgt=step”. polynomial=integer Polynomial degree. For use when Almon/PDL weighting is employed. beta=arg Beta function restriction: none (“none”), trend coefficient equals 1 (“trend”), endpoints coefficient equals 0 (“end- point”), both trend and endpoints restriction (“both”). For use when “midwgt=beta”. The default is “beta=none”.
- 424. midas—417 optmethod = arg Optimization method for nonlinear estimation: “bfgs” (BFGS); “newton” (Newton-Raphson), “opg” or “bhhh” (OPG or BHHH), “hybrid” (initial BHHH followed by BFGS). Hybrid is the default method. optstep = arg Step method for nonlinear estimation: “marquardt” (Mar- quardt); “dogleg” (Dogleg); “linesearch” (Line search). Marquardt is the default method. cov=arg Covariance method for nonlinear models: “ordinary” (default method based on inverse of the estimated informa- tion matrix), “huber” or “white” (Huber-White sandwich). covinfo = arg Information matrix method for nonlinear models: “opg” (OPG); “hessian” (observed Hessian). nodf Do not perform degree of freedom corrections in computing coefficient covariance matrix. The default is to use degree of freedom corrections. m=integer Set maximum number of iterations. c=scalar Set convergence criterion. The criterion is based upon the maximum of the percentage changes in the scaled coeffi- cients. The criterion will be set to the nearest value between 1e-24 and 0.2. s Use the current coefficient values in estimator coefficient vector as starting values in nonlinear estimation (see also param (p. 453) of the Command and Programming Ref- erence). s=number Determine starting values for nonlinear estimation.. Specify a number between zero and one representing the fraction of preliminary EViews chosen values. Note that out of range values are set to “s=1”. Specifying “s=0” initializes coefficients to zero. By default EViews uses “s=1”. showopts / -showopts [Do / do not] display the starting coefficient values and estimation options in the estimation output. coef=arg Specify the name of the coefficient vector (if specified by list); the default behavior is to use the “C” coefficient vec- tor. prompt Force the dialog to appear from within a program. p Print estimation results.
- 425. 418—Chapter 12. Command Reference Examples midas(fixedlag=9, midwgt=beta, beta=endpoint) realgdp c realgdp(- 1) @ monthlypageemp(-5) estimates a MIDAS beta weight specification using the low frequency dependent variable REALGDP and regressors C and REALGDP(-1), and 9 beta weighted lags of EMP(-5) from the “monthlypage” workfile page. The beta weight function places zero restrictions on the endpoint coefficient. midas(maxlag=12, lag=auto) realgdp c realgdp(-1) @ monthlypageemp(-5) estimates the same equation using PDL/Almon weights. The number of lags is chosen auto- matically with a maximum of 12 lags. Cross-references Chapter 1. “MIDAS Regression,” on page 297 of the User’s Guide II discusses the specifica- tion and estimation of MIDAS regression models in EViews. Opens a program file, or text (ASCII) file. This command should be used to open program files or text (ASCII) files for editing. You may also use the command to open workfiles or databases. This use of the open com- mand for this purposes is provided for backward compatibility. We recommend instead that you use the new commands wfopen (p. 515) and pageload (p. 440) to open a workfile, and dbopen (p. 344) to open databases. Syntax open(options) [path]file_name You should provide the name of the file to be opened including the extension (and option- ally a path), or the file name without an extension but with an option identifying its type. Specified types always take precedence over automatic type identification. If a path is not provided, EViews will look for the file in the default directory. Files with the “.PRG” extension will be opened as program files, unless otherwise specified. Files with the “.TXT” extension will be opened as text files, unless otherwise specified. For backward compatibility, files with extensions that are recognized as database files are opened as EViews databases, unless an explicit type is specified. Similarly, files with the extensions “.WF” and “.WF1”, and foreign files with recognized extensions will be opened as workfiles, unless otherwise specified. open Object Container, Data, and File Commands || Programming Commands
- 426. optimize—419 All other files will be read as text files. Options Examples open finfile.txt opens a text file named “Finfile.TXT” in the default directory. open "c:program filesmy filestest1.prg" opens a program file named “Test1.PRG” from the specified directory. open a:mymemo.tex opens a text file named “Mymemo.TEX” from the A: drive. Cross-references See wfopen (p. 515) and pageload (p. 440) for commands to open files as workfiles or workfile pages, and dbopen (p. 344) for opening database files. Find the solution to a user-defined optimization problem. The optimize command calls the EViews optimizer to find the optimal control values for a subroutine defined elsewhere in the program file or files. Syntax optimize(options) subroutine_name(arguments) You should follow the optimize command keyword with options and subroutine_name, the name of a defined subroutine in your program (or included programs). The subroutine must contain at least two arguments. By default EViews will interpret the first argument as the output of the subroutine and will use it as the value to optimize. If the objective contains more than one value, as in a series or vector, EViews will optimize the sum of the values. The second argument is, by default, used to define the control parameters for which EViews will find the optimal values. p Open file as program file. t Open file as text file. type=arg (“prg” or “txt”) Specify text or program file type using keywords. optimize Programming Commands
- 427. 420—Chapter 12. Command Reference Since the objective and controls are defined by a standard EViews subroutine, the arguments of the subroutine may correspond to numbers, strings, and various EViews objects such as series, vectors, scalars. Options Optimization Objective and Controls The default optimization objective is to maximize the first argument of the subroutine. You may use the following optimization options to change the optimization objective and to specify the coefficients (control parameters): max [=integer] Maximize the subroutine objective or sum of the values of the subroutine objective (default). By default the first argument of the subroutine is used as the maximization objective. You may change the objective to another argument by specifying an integer argument location. min [=integer] Minimize the subroutine objective or sum of the values of the subroutine objective. By default the first argument of the subroutine is used as the minimization objective. You may change the objective to another argument by specifying an integer argument location. ls [=integer] Perform least squares minimization of the sum of squared values of the subroutine objective. (The objective argu- ment cannot be a scalar value when using this option.) By default the first argument of the subroutine is used as the minimization objective. You can change the objective to another argument by specifying an integer argument loca- tion. ml [=integer] Perform maximum likelihood estimation of the sum of the values of the subroutine objective. (The objective argu- ment cannot be a scalar value when using this option.) By default the first argument of the subroutine is used as the maximization objective. You can change the objective to another argument by specifying an integer argument location. Note that the “ml” option specifies the same optimization as when using the “max” option, but permits a different set of Hessian matrix choices.
- 428. optimize—421 Optimization Options coef=integer (default = 2) Specify the argument number of the function that contains the coefficient controls for the optimization. If the argument is a vector or matrix, each element of the vector or matrix will be treated as a coefficient. If the argu- ment is a series, each element of the series within the cur- rent workfile sample will be treated as a coefficient. The default value is 2 so that the second argument is assumed to contain the coefficient controls. grads=integer Specifies an argument number corresponding to analytic gradients for each of the coefficients. If this option is not specified, gradients are evaluated numerically. Available for “ls” and “ml” estimation. • If the objective argument is a scalar, the gradient argument should be a vector of length equal to the num- ber of elements in the coefficient argument. • If the objective argument is a series, the gradient argument should be a group object containing one series per element of the coefficient argument. The series obser- vations should contain the corresponding derivatives for each observation in the current workfile sample. • For a vector objective, the gradient argument should be a matrix with number of rows equal to the length of the objective vector, and columns equal to the number of elements in the coefficient argument. • “grad=” may not be specified if the objective is a matrix. hess=arg Specify the type of Hessian approximation: “numeric” (numerical approximation), “bfgs” (Broyden–Fletcher– Goldfarb–Shanno), or “opg” (outer product of gradients, or BHHH). “opg” is only available when using “ls” or “ml” optimiza- tion. The default value is “bfgs” unless using “ls” optimiza- tion, which defaults to “opg”. step=arg (default_= “marquardt”) Set the step method: “marquardt”, “dogleg” or “line- search”. scale=arg Set the scaling method: “maxcurve” (default), or “none”. m=int Set the maximum number of iterations c=number Set the convergence criteria.
- 429. 422—Chapter 12. Command Reference Examples The first example estimates a regression model using maximum likelihood. The subroutine LOGLIKE first computes the regression residuals using the coefficients in the vector BETA along with the dependent variable series given by DEP and the regressors in the group REGS. subroutine loglike(series logl, vector beta, series dep, group regs) series r = dep - beta(1) - beta(2)*regs(1) - beta(3)*regs(2) - beta(4)*regs(3) logl = @log((1/beta(5))*@dnorm(r/beta(5))) endsub series LL vector(5) MLCoefs MLCoefs = 1 MLCoefs(5) = 100 optimize(ml=1, finalh=mlhess, hess=numeric) loglike(LL, MLCoefs, y, xs) trust=number (default=0.25) Sets the initial trust region size as a proportion of the initial control values. Smaller values of this parameter may be used to provide a more cautious start to the optimization in cases where larger steps immediately lead into an undesirable region of the objective. Larger values may be used to reduce the iteration count in cases where the objective is well behaved but the initial values may be far from the optimum values. deriv=high Always use high precision numerical derivatives. Without this option, EViews will start by using lower precision derivatives, and switch to higher precision as the optimiza- tion progresses. feps=number (default=2.2e-16) Set the expected relative accuracy of the objective function. The value indicates what fraction of the observed objective value should be considered to be random noise. noerr Turn off error reporting. finalh=name Save the final Hessian matrix into the workfile with name name. For “hess=bfgs”, the final Hessian will be based on numeric derivatives rather than the BFGS approximation used during the optimization since the BFGS approxima- tion need not converge to the true Hessian.
- 430. optimize—423 The optimize command instructs EViews to use the LOGLIKE subroutine for purposes of maximization, and to use maximum likelihood to maximize the sum (over the workfile sam- ple) of the LL series with respect to the five elements of the vector MLCOEFS. EViews employs a numeric approximation to the Hessian in optimization, and saves the final esti- mate in the workfile in the sym object MLHESS. Notice that we specify initial values for the MLCOEFS coefficients prior to calling the optimi- zation routine. Our second example recasts the estimation above as a least squares optimization problem, and illustrates the use of the “grads=” option to employ analytically computed gradients defined in the subroutine. subroutine leastsquareswithgrads(series r, vector beta, group grads, series dep, group regs) r = dep - beta(1) - beta(2)*regs(1) - beta(3)*regs(2) - beta(4)*regs(3) grads(1) = 1 grads(2) = regs(1) grads(3) = regs(2) grads(4) = regs(3) endsub series LSresid vector(4) LSCoefs lscoefs = 1 series grads1 series grads2 series grads3 series grads4 group grads grads1 grads2 grads3 grads4 optimize(ls=1, grads=3) leastsquareswithgrads(LSresid, lscoefs, grads, y, xs) Note that for a linear least squares problem, the derivatives of the coefficients are trivial - the regressors themselves (and a series of ones for the constant). The next example uses matrix computation to obtain an optimizer objective that finds the solution to the same least squares problem. While the optimizer is not a solver, we can trick it into solving that equation by creating a vector of residuals equal to , and asking the optimizer to find the values of that minimize the square of those residuals: subroutine local matrixsolve(vector rvec, vector beta, series dep, group regs) stom(regs, xmat) xmat = @hcat(@ones(100), xmat) stom(dep, yvec) XX b XY– b
- 431. 424—Chapter 12. Command Reference rvec = @transpose(xmat)*xmat*beta - @transpose(xmat)*yvec rvec = @epow(rvec,2) endsub vector(4) MSCoefs MSCoefs = 1 vector(4) rvec optimize(min=1) matrixsolve(rvec, mscoefs, y, xs) The first few lines of the subroutine convert the input dependent variable series and regres- sor group into matrices. Note that the regressor group does not contain a constant term upon input, so we append a column of ones to the regression matrix XMAT, using the @hcat command. Lastly, we define a subroutine containing the quadratic form, and use the optimize com- mand to find the value that minimizes the function: subroutine f(scalar !y, scalar !x) !y = 5*!x^2 - 3*!x - 2 endsub scalar in = 0 scalar out = 0 optimize(min) f(out, in) The subroutine F calculates the simple quadratic formula: (12.1) which attains a minimum of -2.45 at an IN value of 0.3. Cross-references For discussion, see Chapter 10. “User-Defined Optimization,” beginning on page 239. Save the current EViews global options settings “.INI” files into a directory. Syntax optsave directory Save a copy of the current global options settings into the specified directory. Usually this command will be used in conjuncture with a later use of the optset command. Any exist- ing option settings in the directory will be overwritten. “General Options” and “Graphics Defaults” will always be saved. “Database registry set- tings” and “Database object aliases” will only be saved if the file location setting for the optsave Object Container, Data, and File Commands Y 5X 2 3X– 2–
- 432. optset—425 “Database Registry” and “Alias Map Path” is the same as the file location of the INI File Path. If the directory name is omitted, the option settings currently in effect will be used to replace the default global options. (This can be used to copy option settings back into your default settings after the optset command has switched to using options in a different directory). Note that this command does not change which set of options are active. You must follow this command with the optset command if you would like to switch to using the saved copy as your active set of options. Cross-references See Appendix A. “Global Options,” beginning on page 813 of User’s Guide I for discussion of the global options. See also optset (p. 425). Replace the current EViews global options settings “.INI” files with ones based in a differ- ent directory. Syntax optset directory Temporarily switch to using the global options settings stored within “.INI” files in the spec- ified directory. These will typically have been previously saved by using the optsave com- mand. “General Options” and “Graphics Defaults” will always be switched. “Database registry set- tings” and Database object aliases” will only be switched if the file location setting for the “Database Registry” and “Alias Map Path” is the same as the file location of the INI File Path. The new options will stay in effect until EViews is restarted or until the optset command is executed again with a different directory. After the optset command has been issued, changing settings using the Options menu will modify settings in the new directory. If the directory name is omitted, the global options settings will be reset to use the settings from the default location (the same as restarting EViews). Note that you can use the command “optset .” in a program to switch to using the global options saved in the same directory as the program file. This can be used to ensure that multiple users always use the same global options settings when running a shared pro- gram. optset Object Container, Data, and File Commands
- 433. 426—Chapter 12. Command Reference Cross-references See Appendix A. “Global Options,” beginning on page 813 of User’s Guide I for discussion of the global options. See also optsave (p. 424). Estimation of ordered dependent variable models. Syntax equation name.ordered(options) y x1 [x2 x3 ...] equation name.ordered(options) specification Options ordered Interactive Use Commands d=arg (default=“n”) Specify likelihood: normal likelihood function, ordered pro- bit (“n”), logistic likelihood function, ordered logit (“l”), Type I extreme value likelihood function, ordered Gompit (“x”). optmethod = arg Optimization method: “bfgs” (BFGS); “newton” (Newton- Raphson), “opg” or “bhhh” (OPG or BHHH), “legacy” (EViews legacy). Newton-Raphson is the default method. optstep = arg Step method: “marquardt” (Marquardt); “dogleg” (Dog- leg); “linesearch” (Line search). Marquardt is the default method. cov=arg Covariance method: “ordinary” (default method based on inverse of the estimated information matrix), “huber” or “white” (Huber-White sandwich method)., “glm” (GLM method).. covinfo = arg Information matrix method: “opg” (OPG); “hessian” (observed Hessian). (Applicable when non-legacy “optmethod=”.) h Huber-White quasi-maximum likelihood (QML) standard errors and covariances. (Legacy option Applicable when “optmethod=legacy”). m=integer Set maximum number of iterations.
- 434. ordered—427 If you choose to employ user specified starting values, the parameters corresponding to the limit points must be in ascending order. Examples ordered(d=l,cov=huber) y c wage edu kids estimates an ordered logit model of Y on a constant, WAGE, EDU, and KIDS with QML stan- dard errors. This command uses the default quadratic hill climbing algorithm. param c(1) .1 c(2) .2 c(3) .3 c(4) .4 c(5).5 equation eq1.binary(s) y c x z coef betahat = @coefs estimates an ordered probit model of Y on a constant, X, and Z from the specified starting values. The estimated coefficients are then stored in the coefficient vector BETAHAT. Cross-references See “Ordered Dependent Variable Models” on page 334 of the User’s Guide II for additional discussion. See also binary (p. 299). See Equation::ordered (p. 135) for the equivalent equation object command. c=scalar Set convergence criterion. The criterion is based upon the maximum of the percentage changes in the scaled coeffi- cients. The criterion will be set to the nearest value between 1e-24 and 0.2. s Use the current coefficient values in “C” as starting values (see also param (p. 453)). s=number Specify a number between zero and one to determine start- ing values as a fraction of preliminary EViews default val- ues (out of range values are set to “s=1”). showopts / -showopts [Do / do not] display the starting coefficient values and estimation options in the estimation output. coef=arg Specify the name of the coefficient vector (if specified by list); the default behavior is to use the “C” coefficient vec- tor. prompt Force the dialog to appear from within a program. p Print results.
- 435. 428—Chapter 12. Command Reference Redirect printer output. You may specify that any procedure that would normally send output to the printer puts out- put in a text, Rich Text Format (RTF), or comma-separated value (CSV) file, in a spool object, or into frozen table or graph objects in the current workfile. Syntax output[(f)] base_name output(options) [path]file_name output[(s)] spool_name output off By default, the output command redirects the output into frozen objects. You should supply a base name after the output keyword. Each subsequent print command will create a new table or graph object in the current workfile, using the base name and an identifying num- ber. For example, if you supply the base name of “OUT”, the first print command will gener- ate a table or graph named OUT01, the second print command will generate OUT02, and so on. You can also use the optional settings, described below, to redirect table and text output to a text, RTF, or CSV file, or all output (including graphs) to an RTF file. If you elect to redirect output to a file, you must specify a filename. You can use the “s” option, described below, to redirect all output to a spool object. When followed by the optional keyword off, the output command turns off output redirec- tion. Subsequent print commands will be directed to the printer. Options output Programming Commands f Redirect all output to frozen objects in the default workfile, using base_name. t Redirect table and text output to a text file. Graphic output will still be sent to the printer. r Redirect all output to an Rich Text Format (RTF) file. v Redirect all output to an comma-separated value (CSV) file.
- 436. output—429 Examples output print_ causes the first print command to generate a table or graph object named PRINT_01, the sec- ond print command to generate an object named PRINT_02, and so on. output(t) c:dataresults equation eq1.ls(p) log(gdp) c log(k) log(l) eq1.resids(g,p) output off The first line redirects printing to the RESULTS.TXT file, while the print option of the second and third lines sends the graph output to the printer. The last line turns output redirection off and restores normal printer use. If instead, the first line read: output(r) c:dataresults all subsequent output would be sent to the RTF file RESULTS.RTF. output(s) mySpool redirects all output to the MYSPOOL spool. If the spool already exists, printed objects will be appended to the end of the spool. Cross-references See “Print Setup,” beginning on page 833 of User’s Guide I for further discussion. See also pon (p. 718), poff (p. 718). s Redirect all output to a spool object. o Overwrite file if necessary. If the specified filename for the text, RTF, or CSV file exists, overwrite the file. The default is to append to the file. Only Applicable when text, RTF, or CSV file output (speci- fied using options “t”, “r”, or “v”). c Command logging. Output both the output, and the com- mand used to generate the output. This option is only applicable in a program when used in conjunction with the pon (p. 718) command which enables automatic printing of output. The use of pon is not required when output is specified in a command window. Only Applicable when text, RTF, or CSV file output (speci- fied using options “t”, “r”, or “v”).
- 437. 430—Chapter 12. Command Reference Append contents of the specified workfile page to the active workfile page. Syntax pageappend(options) wfname[pgname] [object_list] where wfname is the name of a workfile that is currently in memory. You may optionally provide the name of a page in wfname that you wish to used as a source, and the list of objects to be read from the source workfile page. If no wfname is provided, EViews will use the default page in the source workfile. The command appends the contents of the source page to the active page in the default workfile. The target page is first unstructured (if necessary) and its range is expanded to encompass the combined range of the sample from the source workfile page, and the desti- nation page. The default behavior is to append all series and alpha objects (but not other types) from the source page, but the optional object_list may be provided to specify specific series, or to specify objects other than series or alpha objects to be included. Command options may also be used to modify the list of objects to be included. Note that since this operation is performed in place, the original workfile page cannot be recovered. We recommend that you consider backing up your original page using pagecopy (p. 432). Options pageappend Object Container, Data, and File Commands smpl=smpl_spec Specifies an optional sample identifying which observa- tions from the source page are to be appended. Either pro- vide the sample range in double quotes or specify a named sample object. The default is “@all”. allobj Specifies that all objects (including non-series and non- alpha objects) should be appended. For objects other than series and alphas, appending involves simply copying the objects from the source page to the destination page. This option may not be used with an explicit object_list specifi- cation. match Specifies that only series and alphas in the append page that match series and alphas of the same name in the active page should be appended. This option may not be used with “allobj” or with an explicit object_list specifica- tion.
- 438. pagecontract—431 Examples pageappend updates appends, to the default workfile page, all of observations in all of the series in the active page of the workfile UPDATES. pageappend(match, smpl="1999 2003") updates restricts the series to those which match (by name) those in the default workfile page, and restricts the observations to merge to those between 1999 and 2003. pageappend newdatpage1 income cons takes only the INCOME and CONS series from the PAGE1 of the NEWDATA workfile, and appends them to the current workfile page. pageappend(alltypes, suffix="_1") mydata appends all objects from MYDATA, merging series with matching names, and renaming other matching objects by appending “_1” to the end of the name. Cross-references See “Appending to a Workfile” on page 281 of User’s Guide I for discussion. See also pagecopy (p. 432). Contract the active workfile page according to the specified sample. Syntax pagecontract smpl_spec where smpl_spec is a sample specification. Contraction removes observations not in speci- fied sample from the active workfile page. Note that since this operation is performed in sufix=suffix_arg (default=“_a”) Specifies a string to be added to the end of the source object name, if necessary, to avoid name collision when creating a new object in the target page. obsid=arg Provides the name of a series used to hold the date or observation ID of each observation in the destination work- file. wfid=arg Provides the name of a (boolean) series to hold an indica- tor of the source for each observation in the destination workfile (0, if from the destination; 1, if from the source). pagecontract Object Container, Data, and File Commands
- 439. 432—Chapter 12. Command Reference place, you may wish to backup your original page (see pagecopy (p. 432)) prior to contract- ing. Examples pagecontract if income<50000 and race=2 removes all observations with INCOME values greater than or equal to 50000 and RACE not equal to 2. pagecontract 1920 1940 1945 2000 removes observations for the years 1941 to 1944. Cross-references See “Contracting a Workfile” on page 284 of User’s Guide I for discussion. See also pagecopy (p. 432). Copies all or part of the active workfile page to a new workfile, or to a new page within the default workfile. Syntax pagecopy(options) [object_list] where the optional object_list specifies the workfile objects to be copied. If object_list is not provided, all objects will be copied subject to the option restrictions discussed below. If copying objects to a new page within the default workfile, you may choose to copy series objects (series, alphas, and links) by value or by link (by employing the “bylink” option). If you elect to copy by value, links in the source page will converted to ordinary series and alpha objects when they are copied. If you copy by link, series and alpha objects in the source page are converted to links when copied. The default is to copy by value. If you copy objects to a new workfile, data objects must be copied by value. pagecopy Object Container, Data, and File Commands
- 440. pagecopy—433 Options Examples pagecopy(page=allvalue, wf=newwf) will first create a new workfile named NEWWF, with a page ALLVALUE that has the same structure as the current page. Next, all of the objects in the active workfile page will be cop- ied into the new page, with the series objects copied by value. In contrast, pagecopy(bylink, page=alllink) bylink Specifies that series and alpha objects be copied as links to the source page. This option is not available if you use the “wf=” option, since linking requires that the destination page be in the same workfile as the source page. Automati- cally sets the “dataonly” option so that only series, alphas, links, and valmaps will be copied. smpl=smpl_spec Specifies an optional sample identifying which observa- tions from the source page are to be appended. Either pro- vide the sample range in double quotes or specify a named sample object. The default is “@all”. rndobs=integer Copy only a random subsample of integer observations from the specified sample. Not available with “bylink,” “rndpct,” or “prob.” rndpct=arg Copy only a random percentage subsample of arg (a num- ber between 0 and 100) of the specified sample. Not avail- able with “bylink,” “rndobs,” or “prob.” prob=arg Copies a random subsample where each observation has a fixed probability, prob, of being selected. prob should be entered as a percentage value (a number between 0 and 100). Not available with “bylink,” “rndobs,” or “rndpc” dataonly Only series, alphas, links, and valmaps should be copied. The default is to copy all objects (unless the “bylink” option is specified, in which case only series objects are copied). nolinks Do not copy links from the source page. wf=wf_name Optional name for the destination workfile. If not provided, EViews will create a new untitled workfile. Not available if copying using the “bylink” option. page=page_name Optional name for the newly created page. If not provided, EViews will use the next available name of the form “Unti- tled##”, where ## is a number.
- 441. 434—Chapter 12. Command Reference will instead create a page ALLLINK in the existing workfile, and will copy all series objects by creating links in the new page. pagecopy(page=partcopy, bylink, smpl="1950 2000 if gender=""male""") a* *z will create a new page named PARTCOPY in the existing workfile containing the specified subsample of observations, and will copy all series objects in the current page beginning with the letter “A” or ending with the letter “Z”. The objects will be copied by creating links in the new page. pagecopy(page=rndcopy, smpl="1950 2000 if gender=""male""", rndobs=200, dataonly, nolinks) creates a new workfile and page RNDCOPY containing a 200 observation random sample from the specified subsample. Series and alpha objects only will be copied by value from the source page. Cross-references See “Copying from a Workfile” on page 284 of User’s Guide I for discussion. See also pageappend (p. 430). Create a new page in the default workfile. The new page becomes the active page. Syntax pagecreate(options) freq[(subperiod_opts)] start_date end_date [num_cross_sections] pagecreate(options) u num_observations pagecreate(id_method[,options]) id_list [@srcpage page_list] pagecreate(idcross[,options]) id1 id2 [@srcpage page1 page2] pagecreate(idcross[,options]) id1 @range(freq, start_date, end_date) [@srcpage page1] These different forms of the pagecreate command encompass three distinct approaches to creating a new workfile page: (1) regular frequency description or unstructured data descrip- tion; (2) using the union or intersection of unique values from one or more ID series in exist- ing workfile pages; (3) using the cross of unique values from two identifier series or from an identifier series and a date range. Each of these approaches is described in greater detail below. pagecreate Object Container, Data, and File Commands
- 442. pagecreate—435 Regular Frequency or Unstructured Description The first two forms of the command permit you to create a new workfile page using a regu- lar frequency or unstructured description: • pagecreate(options) freq[(subperiod_opts)] start_date end_date [num_cross_sections] • pagecreate(options) u num_observations The first form of the command should be employed to create a regular frequency page with the specified frequency, start, and end date. If you include the optional argument num_cross_sections, EViews will create a balanced panel page using integer identifiers for each of the cross-sections. Note that more complex panel structures may be defined using pagestruct (p. 448). The second form of the command creates an unstructured workfile with the specified num- ber of observations. Note that these forms of the command are analogous to wfcreate (p. 510) except that instead of creating a new workfile, we create a new page in the default workfile. The freq argument should be specified using one of the following forms: Sec[opt], 5Sec[opt], 15Sec[opt], 30Sec[opt] Seconds in intervals of: 1, 5, 15, or 30 seconds, respec- tively. You may optionally specify days of the week and start and end times during the day using the opt parameter. See explanation of subperiod options below. Min[opt], 2Min[opt], 5Min[opt], 10Min[opt], 15Min[opt], 20Min[opt], 30Min[[opt] Minutes in intervals of: 1, 2, 5, 10, 15, 20, or 30 minutes, respectively. You may optionally specify days of the week and start and end times during the day using the opt parameter. See explanation of subperiod options below. H[opt], 2H[opt], 4H[opt], 6H[opt], 8H[opt], 12H[[opt] Hours in intervals of: 1, 2, 4, 6, 8, or 12 hours, respectively. You may optionally specify days of the week and start and end times during the day using the opt parameter. See explanation of subperiod options below. D(s, e) Daily with arbitrary days of the week. Specify the first and last days of the week with integers s and e, where Monday is given the number 1 and Sunday is given the number 7. (Note that the “D” option used to specify a 5-day frequency in versions prior to EViews 7). D5 or 5 Daily with five days per week, Monday through Friday. D7 or 7 Daily with seven days per week.
- 443. 436—Chapter 12. Command Reference Subperiod options EViews allows for setting the days of the week and the time of day within intraday frequen- cies, which include seconds, minutes, and hours. For instance, you may specify hourly data between 8AM and 5PM on Monday through Wednesday. These subperiod options should follow the frequency keyword and be enclosed in parentheses. To specify days of the week, use integers to indicate the days, where Monday is given the number 1 and Sunday is given the number 7. For example, pagecreate(wf=strhours) 30MIN(1-6, 8:00-17:00) 1/3/2000 12/30/2000 indicates a half-hour frequency that includes Monday through Saturday from 8AM to 5PM. To specify the start and end times, you may use either a 24 hour clock, including minutes and optionally seconds, or a 12 hour clock using AM and PM. For example, each of the fol- lowing represents 8PM: 8PM, 8:00PM, 8:00:00PM, 20:00, and 20:00:00. Thus, our previous example could have been written: pagecreate(wf=strhours) 30MIN(1-6, 8AM-5PM) 1/3/2000 12/30/2000 Note that day and time ranges may be delimited by either commas or dashes. So this com- mand is also equivalent to: pagecreate(wf=strhours) 30MIN(1,6, 8AM,5PM) 1/3/2000 12/30/2000 though you will likely find the dashes easier to read. If you wish to include all days of the week but would like to specify a start and end time, set the date range to include all days and then specify the times. The day of the week parameter appears first and is required if you wish to supply the time of day parameters. For instance, pagecreate(wf=storehours) 30MIN(1-7, 10AM-3PM) 1/3/2000 12/30/2000 indicates a half-hour frequency from 10AM to 3PM on all days of the week. W Weekly T Ten-day (daily in intervals of ten). F Fortnight BM Bimonthly M Monthly Q Quarterly S Semi-annual A or Y Annual 2Y, 3Y, 4Y, 5Y, 6Y, 7Y, 8Y, 9Y, 10Y, 20Y Multi-year in intervals of: 2, 3, 4, 5, 6, 7, 8, 9, 10, or 20 years, respectively.
- 444. pagecreate—437 You may also include a time with the start and end date parameters to specify partial days at the beginning or end of the workfile. For example, pagecreate(wf=strhours) 30MIN(1-6, 8AM-5PM) 1/3/2000 10AM 12/30/2000 2PM creates the same workfile page as above, but limits the first day, 1/3/2000, to 10AM - 5PM and the last day, 12/30/2000, to 8AM - 2PM. Unique Values from a Set of Identifier Series The next form of the command allows for creating pages from the unique values of one or more identifier series found in one or more workfile pages: • pagecreate(id_method[,options]) identifier_list [@srcpage page_list] The identifier_list should include one or more ID series. If more than one ID series is pro- vided, EViews will use the values that are unique across all of the series. If you wish to cre- ate a page with a date structure, you should specify one of your identifiers using the special “@DATE” keyword identifier, enclosing the series (or the date ID component series) inside parentheses. If you wish to use the date ID values from the source workfile page, you may use the “@DATE” keyword without arguments. The id_method describes how to handle unique ID values that differ across multiple pages: If the optional source page or list of source pages is not provided, EViews will use the default workfile page. Note that if a single workfile page is used, the two ID methods yield identical results. Cross of Unique Values from Two Identifier Series or from an Identifier Series and a Date Range The last two forms of the command create a new page by crossing the unique values in two ID series located in one or more pages, or by crossing an ID series from one page with a date range. First, you may specify a pair of identifiers, and optionally source pages where they are located, • pagecreate(idcross[,options]) id1 id2 [@srcpage page1 page2] You may instruct EViews to create a date structured page by specifying one of your two iden- tifiers using a “@DATE” specification as described above. id Use the observed values of the series in the identifier_list in specified page. idunion Use the union of the observed values of the series in the identifier_list in the specified pages. idintersect Use the intersection of the observed values of the series in the identifier_list in the specified pages.
- 445. 438—Chapter 12. Command Reference Alternately, you may provide a single identifier and a range specification using the “@RANGE” keyword with a freq, start_date, and end_date, and optionally, the location of the identifier series. • pagecreate(idcross[,options]) id1 @range(freq, start_date, end_date) [@srcpage page1] Options Examples Regular Frequency or Unstructured Description The two commands: pagecreate(page=annual) a 1950 2005 pagecreate(page=unstruct) u 1000 create new pages in the existing workfile. The first page is an annual page named ANNUAL, containing data from 1950 to 2005; the second is a 1000 observation unstructured page named UNSTRUCT. pagecreate(page=mypanel) a 1935 1954 10 creates a new workfile page named MYPANEL, containing a 10 cross-section annual panel for the years 1935 to 1954. pagecreate(page=fourday) D(1,4) 1/3/2000 12/31/2000 specifies a daily workfile page from January 3, 2000 to December 31, 2000, including only Monday through Thursday. The day range may be delimited by either a comma or a dash, such that pagecreate(wf=fourday) D(1-4) 1/3/2000 12/31/2000 is equivalent to the previous command. smpl=smpl_spec Specifies an optional sample identifying which observa- tions to use when creating a page using the id_method option. Either provide the sample range in double quotes or specify a named sample object. The default is “@all”. When multiple source workfiles are involved, the specified sample must be valid for all workfiles. page=page_name Optional name for the newly created page. If not provided, EViews will use the next available name of the form “Unti- tled##”, where ## is a number. wf=wf_name Optional name for the new workfile. If not provided, EViews will create a new page in the default workfile. prompt Force the dialog to appear from within a program.
- 446. pagecreate—439 pagecreate(wf=captimes) 15SEC(2-4) 1/3/2000 12/30/2000 creates a workfile page with 15 second intervals on Tuesday through Thursday only, from 1/3/2000 to 12/30/2000. Unique Values from a Set of Identifier Series pagecreate(id, page=statepage) state creates a new page STATEIND using the distinct values of STATE in the current workfile page. pagecreate(id, page=statepage) state industry creates a new page named STATEIND, using the distinct STATE/INDUSTRY values in the active page. pagecreate(id, page=stateyear) state @date(year) pagecreate(id, page=statemonth) @date(year, month) use STATE, along with YEAR, and the YEAR and MONTH series respectively, to form identi- fiers that will be used in creating the new dated workfile pages. pagecreate(id, smpl="if sex=1") crossid @date creates a new page using CROSSID and existing date ID values of the active workfile page. Note that only observations in the subsample defined by “@all if sex=1” are used to deter- mine the unique values. pagecreate(id, page=AllStates, smpl="if sex=""Female""") stateid @srcpage north south east west creates a new page ALLSTATES structured using the union of the unique values of STATEID from the NORTH, SOUTH, EAST and WEST workfile pages that are in the sample “if sex="Female"”. Note the use of the double quote escape character for the double quotes in the sample string. pagecreate(idintersect, page=CommonStates, smpl="1950 2005") stateid @srcpage page1 page2 page3 creates a new page name COMMONSTATES structured using the intersection of the unique values of STATEID taken from the pages PAGE1, PAGE2, and PAGE3. Cross of Unique Values from Two Identifier Series or from an Identifier Series and a Date Range pagecreate(idcross,page=UndatedPanel) id1 id2 @srcpage page1 page2 will add the new page UNDATEDPANEL to the current workfile. UNDATEDPANEL will be structured as an undated panel using values from the cross of ID1 from PAGE1 and ID2 from PAGE2. To create a dated page using the “idcross” option, you must tag one of the identifiers using an “@DATE” specification:
- 447. 440—Chapter 12. Command Reference pagecreate(idcross,page=AnnualPanel) id1 @date(year) @srcpage page1 page2 You may also specify the cross of an identifier with a date range: pagecreate(idcross,page=QuarterlyPanel) id1 @range(q, 1950, 2006) @srcpage page1 creates a quarterly panel page named QUARTERLYPANEL using the cross of ID1 taken from PAGE1, and quarterly observations from 1950q1 to 2006q4. Cross-references See “Creating a Workfile,” on page 42 of User’s Guide I for discussion. See also wfcreate (p. 510) and pagedelete (p. 440). Delete the named pages from the default workfile. Syntax pagedelete [pgname1 pgname2 pgname3...] By default pagedelete will delete the currently active page in the default workfile. You may specify other pages to delete by listing them, in a space delimited list, after the pagedelete command. Examples pagedelete page1 page2 Cross-references See also pagecreate (p. 434). Load one or more new pages in the default workfile. Syntax pageload [path]workfile_name [page1] [page2] [...] pageload(options) source_description [@keep keep_list] [@drop drop_list] [@keep- map keepmap_list] [@dropmap dropmap_list] [@selectif condition] pageload(options)source_description table_description [@keep keep_list] [@drop drop_list] [@keepmap keepmap_list] [@dropmap dropmap_list] [@selectif con- dition] pagedelete Object Container, Data, and File Commands pageload Object Container, Data, and File Commands
- 448. pagerefresh—441 The basic syntax for pageload follows that of wfopen (p. 515). The difference between the two commands is that pageload creates a new page in the default workfile, rather than open- ing or creating a new workfile. If a page is loaded with a name that matches an existing page, EViews will rename the new page to the next available name (e.g., “INDIVID” will be renamed “INDIVID1”. If a workfile is provided as the source file, EViews will, by default, open all pages in the source workfile. Specific pages may be loaded by providing their names. Examples pageload "c:my documentsdatapanel1" loads the workfile PANEL1.WF1 from the specified directory. All of the pages in the workfile will be loaded as new pages into the current workfile. pageload f.wf1 mypage loads the page mypage in the workfile F.WF1 located in the default directory. See the extensive set of examples in wfopen (p. 515). Cross-references See “Creating a Page by Loading a Workfile or Data Source” on page 85 of User’s Guide I for discussion. See also wfopen (p. 515) and pagecreate (p. 434). Refresh all links and auto-series in the active workfile page. Primarily used to refresh links that use external database data. Syntax pagerefresh Cross-references See “Creating a Database Link” on page 366 and “Understanding the Cache” on page 367 of User’s Guide I. See also Chapter 8. “Series Links,” on page 223 of User’s Guide I for a description of link objects, and “Auto-Updating Series” on page 193 of User’s Guide I for a discussion of auto- updating series. See wfrefresh (p. 528) to reference an entire workfile. See also Link::link (p. 352) and Link::linkto (p. 353) in the Object Reference. pagerefresh Object Container, Data, and File Commands
- 449. 442—Chapter 12. Command Reference Rename the specified workfile page. Syntax pagerename old_name new_name renames the old_name page in the default workfile to new_name. Page names are case- insensitive for purposes of comparison, even though they are displayed using the input case. Examples pagerename Page1 StateByYear Cross-references See also pagecreate (p. 434). Save the active page in the default workfile as an EViews workfile (.WF1 file) or as a for- eign data source. Syntax pagesave(options) [path]filename pagesave(options) source_description [@keep keep_list] [@drop drop_list] [@keep- map keepmap_list] [@dropmap dropmap_list] [@smpl smpl_spec] pagesave(options) source_description table_description [@keep keep_list] [@drop drop_list] [@keepmap keepmap_list] [@dropmap dropmap_list] [@smpl smpl_spec] The command saves the active page in the specified directory using filename. By default, the page is saved as an EViews workfile, but options may be used to save all or part of the page in a foreign file or data source. See wfopen (p. 515) for details on the syntax of source_de- scriptions and table_descriptions. Note, in particular, that you may use the byrow table_de- scription for Excel 2007 files to instruct EViews to save the series by row (instead of the standard by column). pagerename Object Container, Data, and File Commands pagesave Object Container, Data, and File Commands
- 450. pagesave—443 Options The following table summaries the various foreign formats, along with the corresponding “type=” keywords: type=arg, t=arg Optional type specification: (see table below). Note that ODBC support is provided only in the EViews Enterprise Edition. mode=arg Specify whether to create a new file, overwrite an existing file, or update an existing file. arg may be “create” (create new file only; error on attempt to overwrite) or “update” (only available for Excel files, and only if Excel is installed) (update an existing file, only overwriting the area specified by the range= table_description). If the “mode=” option is not used, EViews will create a new file, unless the file already exists in which case it will overwrite it. maptype=arg Write selected maps as: numeric (“n”), character (“c”), both numeric and character (“b”). nomapval Do not write mapped values for series with attached value labels (the default is to write mapped values) noid Do not write observation identifiers to foreign data files (by default, EViews will include a column with the date or observation identifier). nonames Do not export variable names. Option Keywords Access “access” Aremos-TSD “a”, “aremos”, “tsd” Binary “binary” dBASE “dbase” Excel (through 2003) “excel” EViews Workfile --- Gauss Dataset “gauss” GiveWin/PcGive “g”, “give” HTML “html” Lotus 1-2-3 “lotus” ODBC Dsn File “dsn” ODBC Data Source “odbc” MicroTSP Workfile “dos”, “microtsp”
- 451. 444—Chapter 12. Command Reference Note that if you wish to save your Excel 2007 XML file with macros enabled, you should specify an explicit filename extension of “.XLSM”. Examples pagesave new_wf saves the current EViews workfile page as “New_wf.WF1” in the default directory. pagesave "c:documents and settingsmy dataconsump" saves the current workfile page as “Consump.WF1” in the specified path. To save the current page in a macro-enabled Excel 2007 file, you should specify the explicit filename extension “.XLSM”: pagesave(type=excelxml, mode=update) range=”Sheet2!a1” byrow macro.xlsm @keep gdp unemp Cross-references See “Saving a Workfile” on page 75 in the User’s Guide I. See also wfopen (p. 515) and wfsave (p. 529). Make the specified page in the default workfile the active page. Syntax pageselect pgname where pgname is the name of a page in the default workfile. MicroTSP Mac Workfile “mac” RATS 4.x “r”, “rats” RATS Portable / TROLL “l”, “trl” SAS Program “sasprog” SAS Transport “sasxport” SPSS “spss” SPSS Portable “spssport” Stata (Version 7 Format) “stata” Text / ASCII “text” TSP Portable “t”, “tsp” pageselect Object Container, Data, and File Commands
- 452. pagestack—445 Examples pageselect page2 changes the active page to PAGE2. Cross-references See also wfselect (p. 532). Create a panel structured workfile page using series, alphas, or links from the default workfile page (convert repeated series to repeated observations). Series in the new panel workfile may be created by stacking series, alphas, and links whose names contain a pattern (series with names that differ only by a “stack identifier”), or by repeating a single series, alpha, or link, for each value in a set of stack identifiers. Syntax pagestack(options) stack_id_list [@ series_to_stack] pagestack(options) pool_name [@ series_to_stack] pagestack(options) series_name_pattern [@ series_to_stack] The resulting panel workfile will use the identifiers specified in one of the three forms of the command: • stack_id_list includes a list of the ID values (e.g., “US UK JPN”). • pool_name is the name of a pool object that contains the ID values. • series_name_pattern contains an expression from which the ID values may be deter- mined. The pattern should include the “?” character as a stand in for the parts of the series names containing the stack identifiers. For example, if “CONS?” is the series_name_pattern, EViews will find all series with names beginning with “CONS” and will extract the IDs from the trailing parts of the observed names. The series_to_stack list may contain two types of entries: stacked series (corresponding to sets of series, alphas, and links whose names contain the stack IDs) and simple series (other series, alphas, and links). To stack a set of series whose names differ only by the stack IDs, you should enter an expression that includes the “?” character in place of the IDs. You may list the names of a single stacked series (e.g., “GDP?” or “?CONS”), or you may use expressions containing the wildcard character “*” (e.g., “*?” and “?C*”) to specify multiple series. pagestack Object Container, Data, and File Commands
- 453. 446—Chapter 12. Command Reference By default, the stacked series will be named in the new workfile using the base portion of the series name (if you specify “?CONS” the stacked series will be named “CONS”), and will contain the values of the individual series stacked one on top of another. If one of the indi- vidual series associated with a particular stack ID does not exist, the corresponding stacked values will be assigned the value NA. Individual (simple) series may also be stacked. You may list the names of individual simple series (e.g., “POP INC”), or you can specify your series using expressions containing the wildcard character “*” (e.g., “*”, “*C”, and “F*”). A simple series will be stacked on top of itself, once for each ID value. If the target workfile page is in the same workfile, EViews will create a link in the new page; otherwise, the stacked series will contain (repeated) copies of the original values. When evaluating wildcard expressions, stacked series take precedence over simple series. This means that simple series wildcards will be processed using the list of series not already included as a stacked series. If the series_to_stack list is not specified, the expression “*? *”, is assumed. Options Examples Consider a workfile that contains the seven series: GDPUS, GDPUK, GDPJPN, CONSUS, CONSUK, CONSJPN, CONSFR, and WORLDGDP. pagestack us uk jpn @ *? ?=name_patt, idre- place = name_patt Specifies the characters to use instead of the identifier, ”?”, in naming the stacked series. By default, the name_patt is blank, indicating, for exam- ple, that the stacked series corresponding to the pattern “GDP?” will be named “GDP” in the stacked workfile page. If pattern is set to “STK”, the stacked series will be named GDPSTK. interleave Interleave the observations in the destination stacked workfile (stack by using all of the series values for the first source observation, followed by the values for the second observation, and so on). The default is to stack observa- tions by identifier (stack the series one on top of each other). wf=wf_name Optional name for the new workfile. If not provided, EViews will create a new page in the default workfile. page=page_name Optional name for the newly created page. If not provided, EViews will use the next available name of the form “Unti- tled##”, where ## is a number.
- 454. pagestack—447 creates a new, panel structured workfile page with the series GDP and CONS, containing the stacked GDP? series (GDPUS, GDPUK, and GDPJPN) and stacked CONS? series (CONSUS, CONSUK, and CONSJPN). Note that CONSFR and WORLDGDP will not be copied or stacked. We may specify the stacked series list explicitly. For example: pagestack(page=stackctry) gdp? @ gdp? cons? first determines the stack IDs from the names of series beginning with “GDP”, the stacks the GDP? and CONS? series. Note that this latter example also names the new workfile page STACKCTRY. If we have a pool object, we may instruct EViews to use it to specify the IDs: pagestack(wf=newwf, page=stackctry) countrypool @ gdp? cons? Here, the panel structured page STACKCTRY will be created in the workfile NEWWF. Simple series may be specified by adding them to the stack list, either directly, or using wild- card expressions. Both commands: pagestack us uk jpn @ gdp? cons? worldgdp consfr pagestack(wf=altwf) us uk jpn @ gdp? cons? * stack the various GDP? and CONS? series on top of each other, and stack the simple series GDPFR and WORLDGDP on top of themselves. In the first case, we create a new panel structured page in the same workfile containing the stacked series GDP and CONS and link objects CONSFR and WORLDGDP, which repeat the values of the series. In the second case, the new panel page in the workfile ALTWF will con- tain the stacked GDP and CONS, and series named CONSFR and WORLDGDP containing repeated copies of the values of the series. The following two commands are equivalent: pagestack(wf=newwf) us uk jpn @ *? * pagestack(wf=newwf) us uk jpn Here, every series, alpha, and link in the source workfile is stacked and copied to the desti- nation workfile, either by stacking different series containing the stack_id or by stacking simple series on top of themselves. The “?=” option may be used to prevent name collision. pagestack(?="stk") us uk jpn @ gdp? gdp stacks GDPUS, GDPUK and GDPJPN into a series called GDPSTK and repeats the values of the simple series GDP in the destination series GDP.
- 455. 448—Chapter 12. Command Reference Cross-references For additional discussion, see “Stacking a Workfile” on page 297 in User’s Guide I. See also pageunstack (p. 451). Assign a structure to the active workfile page. Syntax pagestruct(options) [id_list] pagestruct(options) * where id_list is an (optional) list of ID series. The “*” may be used as shorthand for the indi- ces currently in place. If an id_list is provided, EViews will attempt to auto determine the workfile structure. Auto- determination may be overridden through the use of options. If you do not provide an id_list, the workfile will be restructured as a regular frequency workfile. In this case, either the “none” or the “freq=” and “start=” options described below must be provided. Options pagestruct Object Container, Data, and File Commands none Remove the existing workfile structure. freq= arg Specifies a regular frequency; if not provided EViews will auto-determine the frequency. The frequency may be speci- fied as “a” (annual), “s” (semi-annual), “q” (quarterly), “m” (monthly), “w” (weekly), “d” (5-day daily), “7” (7- day daily), or “u” (unstructured/undated). start=arg Start date of the regular frequency structure; if not speci- fied, defaults to “@FIRST”. Legal values for arg are described below. end=arg End date of the regular frequency structure; if not specified, defaults to “@LAST”. Legal values for arg are described below. regular, reg When used with a date ID, this option informs EViews to insert observations (if necessary) to remove gaps in the date series so that it follows the regular frequency calendar. The option has no effect unless a date index is specified.
- 456. pagestruct—449 The values for start and end dates should contain date literals (actual dates or periods), e.g., “1950q1” and “2/10/1951”, “@FIRST”, or “@LAST” (first and last observed dates in the date ID series). Date literals must be used for the “start=” option when restructuring to a regular frequency. In addition, offsets from these date specifications can be specified with a “+” or “–” fol- lowed by an integer: “@FIRST-5”, “@LAST+2”, “1950m12+6”. Offsets are most often used when resizing the workfile to add or remove observations from the endpoints. create Allow creation of a new series to serve as an additional ID series when duplicate ID values are found in any group. EViews will use this new series as the observation ID. The default is to prompt in interactive mode and to fail in programs. balance=arg, bal=arg Balance option (for panel data) describing how EViews should handle data that are unbalanced across ID (cross- section) groups. The arg should be formed using a combination of starts (“s”), ends (“e”), and middles (“m”), as in “balance=se” or “balance=m”. If balancing starts (arg contains “s”), EViews will (if neces- sary) add observations to your workfile so that each cross- section begins at the same observation (the earliest date or observation observed). If balancing ends (arg contains “e”), EViews will add any observations required so that each cross-section ends at the same point (the last date or observation observed). If balancing middles (arg contains “m”) EViews will add observations to ensure that each cross-section has consecu- tive observations from the earliest date or observation for the cross-section to the last date or observation for the cross-section. Note that “balance=m” is implied by the “regular” option. dropna Specifies that observations which contain missing values (NAs or blank strings) in any ID series (including the date or observation ID) be removed from the workfile. If “dro- pna” is not specified and NAs are found, EViews will prompt in interactive mode and fail in programs. dropbad Specifies that observations for which any of the date index series contain values that do not represent dates be removed from the workfile. If “dropbad” is not provided and bad dates are present, EViews will prompt in interac- tive mode and fail in programs.
- 457. 450—Chapter 12. Command Reference Examples pagestruct state industry structures the workfile using the IDs in the STATE and INDUSTRY series. A date ID series (or a series used to form a date ID) should be tagged using the “@DATE” keyword. For example: pagestruct state @date(year) pagestruct(regular) @date(year, month) A “*” may be used to indicate the indices defined in the current workfile structure. pagestruct(end=@last+5) * adds 5 observations to the end of the current workfile. When you omit the id_list, EViews will attempt to restructured the workfile to a regular fre- quency. In this case you must either provide the “freq=” and “start=” options to identify the regular frequency structure, or you must specify “none” to remove the existing structure: pagestruct(freq=a, start=1950) pagestruct(none) Cross-references For extensive discussion, see “Structuring a Workfile,” beginning on page 253 in the User’s Guide I. Break links in all link objects and auto-updating series (formulae) in the active workfile page. You should use some caution with this command as you will not be prompted before the links and auto-updating series are converted. Syntax pageunlink Examples pageunlink breaks links in all pages of the active workfile page. pageunlink Object Container, Data, and File Commands
- 458. pageunstack—451 Cross-references See Chapter 8. “Series Links,” on page 223 of the User’s Guide I for a description of link objects, and “Auto-Updating Series” on page 193 of the User’s Guide I for a discussion of auto-updating series. See also Link::link (p. 352) and Link::linkto (p. 353) in the Object Reference. See unlink (p. 501) and wfunlink (p. 533) for object and workfile based unlinking, respec- tively. Unstack workfile page (convert repeated observations to repeated series). Create a new workfile page by taking series objects (series, alphas, or links) in the default workfile page and breaking them into multiple series (or alphas), one for each distinct value found in a user supplied list of series objects. Typically used on a page with a panel struc- ture. Syntax pageunstack(options) stack_id obs_id [@ series_to_unstack] where stack_id is a single series containing the unstacking ID values used to identify the individual unstacked series, obs_id is a series containing the observation IDs, and series_to_unstack is an optional list of series objects to be copied to the new workfile. Options pageunstack Object Container, Data, and File Commands namepat =name_pattern Specifies the pattern from which unstacked series names are constructed, where “*” indicates the original series name and “?” indicates the stack ID. By default the name_pattern is “*?”, indicating, for exam- ple, that if we have the IDs “US”, “UK”, “JPN”, the unstacked series corresponding to the series GDP should be named “GDPUS”, “GDPUK”, “GDPJPN” in the unstacked workfile page. wf=wf_name Optional name for the new workfile. If not provided, EViews will create a new page in the default workfile. page=page_name Optional name for the newly created page. If not provided, EViews will use the next available name of the form “Unti- tled##”, where ## is a number.
- 459. 452—Chapter 12. Command Reference Examples Consider a workfile that contains the series GDP and CONS which contain the values of Gross Domestic Product and consumption for three countries stacked on top of each other. Suppose further there is an alpha object called COUNTRY containing the observations “US”, “UK”, and “JPN”, which identify which from which country each observation of GDP and CONS comes. Finally, suppose there is a date series DATEID which identifies the date for each observation. The command: pageunstack country dateid @ gdp cons creates a new workfile page using the workfile frequency and dates found in DATEID. The page will contain the 6 series GDPUS, GDPUK, GDPJPN, CONSUS, CONSUK, and CONSJPN corresponding to the unstacked GDP and CONS. Typically the source workfile described above would be structured as a dated panel with the cross-section ID series COUNTRY and the date ID series DATEID. Since the panel has built-in date information, we may use the “@DATE” keyword as the DATEID. The command: pageunstack country @date @ gdp cons uses the date portion of the current workfile structure to identify the dates for the unstacked page. The stack_id must be an ordinary, or an alpha series that uniquely identifies the groupings to use in unstacking the series. obs_id may be one or more ordinary series or alpha series, the combination of which uniquely identify each observation in the new workfile. You may provide an explicit list of series to unstack following an “@” immediately after the obs_id. Wildcards may be used in this list. For example: pageunstack country dateid @ g* c* unstacks all series and alphas that have names that begin with “G” or “C’. If no series_to_unstack list is provided, all series in the source workfile will be unstacked. Thus, the two commands: pageunstack country dateid @ * pageunstack country dateid are equivalent. By default, series are named in the destination workfile page by appending the stack_id val- ues to the original series name. Letting “*” stand for the original series name and “?” for the stack_id, names are constructed as “*?”. This default may be changed using the “name- pat=” option. For example: pageunstack(namepat="?_*") country dateid @ gdp cons
- 460. plot—453 creates the series US_GDP, UK_GDP, JPN_GDP, etc. Cross-references For additional discussion and examples, see “Unstacking a Workfile” on page 291 of the User’s Guide I. See also pagestack (p. 445). Set parameter values. Allows you to set the current values of coefficient vectors. The command may be used to provide starting values for the parameters in nonlinear least squares, nonlinear system esti- mation, and (optionally) ARMA estimation. Syntax param coef_name1 number1 [coef_name2 number2 coef_name3 number3…] List, in pairs, the names of the coefficient vector and its element number followed by the corresponding starting values for any of the parameters in your equation. Examples param c(1) .2 c(2) .1 c(3) .5 resets the first three values of the coefficient vector C. coef(3) beta param beta(2) .1 beta(3) .5 The first line declares a coefficient vector BETA of length 3 that is initialized with zeros. The second line sets the second and third elements of BETA to 0.1 and 0.5, respectively. Cross-references See “Starting Values” on page 48 of the User’s Guide II for a discussion of setting initial val- ues in nonlinear estimation. Line graph. Provided for backward compatibility. See line (p. 924). param Global Commands plot Object Creation Commands
- 461. 454—Chapter 12. Command Reference Sends views of objects to the default printer. Syntax print(options) object1 [object2 object3 …] print(options) object_name.view_command print should be followed by a list of object names or a view of an object to be printed. The list of names must be of the same object type. If you do not specify the view of an object, print will print the default view for the object. Options The default orientation is set by clicking on Print Setup. Examples print gdp log(gdp) d(gdp) @pch(gdp) sends a table of GDP, log of GDP, first difference of GDP, and the percentage change of GDP to the printer. print graph1 graph2 graph3 prints three graphs on a single page. To merge the three graphs, realign them in one row, and print in landscape orientation, you may use the commands: graph mygra.merge graph1 graph2 graph3 mygra.align(3,1,1) print(l) mygra To estimate the equation EQ1 and send the output view to the printer. print eq1.ls gdp c gdp(-1) Cross-references See “Print Setup,” beginning on page 833 of the User’s Guide II for a discussion of print options and the Print Setup dialog. See output (p. 428) for print redirection. print Command Actions p Print in portrait orientation. l Print in landscape orientation.
- 462. qreg—455 Estimation of binary dependent variable models with normal errors. Equivalent to “binary(d=n)”. See binary (p. 299). Declare a program. Syntax program [path]prog_name Enter a name for the program after the program keyword. If you do not provide a name, EViews will open an untitled program window. Programs are text files, not objects. Examples program runreg opens a program window named RUNREG which is ready for program editing. Cross-references See Chapter 6. “EViews Programming,” on page 117 of the User’s Guide I for further details, and examples of writing EViews programs. See also open (p. 418). Estimate a quantile regression specification. Syntax qreg(options) y x1 [x2 x3 ...] qreg(options) linear_specification probit Interactive Use Commands program Programming Commands qreg Interactive Use Commands
- 463. 456—Chapter 12. Command Reference Options quant=number (default = 0.5) Quantile to be fit (where number is a value between 0 and 1). w=arg Weight series or expression. Note: we recommend that, absent a good reason, you employ the default settings Inverse std. dev. weights (“wtype=istdev”) with EViews default scaling (“wscale=eviews”) for backward compatibility with ver- sions prior to EViews 7. wtype=arg (default=“istdev”) Weight specification type: inverse standard deviation (“ist- dev”), inverse variance (“ivar”), standard deviation (“stdev”), variance (“var”). wscale=arg Weight scaling: EViews default (“eviews”), average (“avg”), none (“none”). The default setting depends upon the weight type: “eviews” if “wtype=istdev”, “avg” for all others. cov=arg (default=“sand- wich”) Method for computing coefficient covariance matrix: “iid” (ordinary estimates), “sandwich” (Huber sandwich esti- mates), “boot” (bootstrap estimates). When “cov=iid” or “cov=sandwich”, EViews will use the sparsity nuisance parameter calculation specified in “spmethod=” when estimating the coefficient covariance matrix. bwmethod=arg (default = “hs”) Method for automatically selecting bandwidth value for use in estimation of sparsity and coefficient covariance matrix: “hs” (Hall-Sheather), “bf” (Bofinger), “c” (Cham- berlain). bw =number Use user-specified bandwidth value in place of automatic method specified in “bwmethod=”. bwsize=number (default = 0.05) Size parameter for use in computation of bandwidth (used when “bw=hs” and “bw=bf”). spmethod=arg (default=“kernel”) Sparsity estimation method: “resid” (Siddiqui using residu- als), “fitted” (Siddiqui using fitted quantiles at mean values of regressors), “kernel” (Kernel density using residuals) Note: “spmethod=resid” is not available when “cov=sandwich”. btmethod=arg (default= “pair”) Bootstrap method: “resid” (residual bootstrap), “pair” (xy- pair bootstrap), “mcmb” (MCMB bootstrap), “mcmba” (MCMB-A bootstrap).
- 464. qreg—457 btreps=integer (default=100) Number of bootstrap repetitions btseed=positive integer Seed the bootstrap random number generator. If not specified, EViews will seed the bootstrap random number generator with a single integer draw from the default global random number generator. btrnd=arg (default=“kn” or method previously set using rndseed (p. 463)). Type of random number generator for the bootstrap: improved Knuth generator (“kn”), improved Mersenne Twister (“mt”), Knuth’s (1997) lagged Fibonacci generator used in EViews 4 (“kn4”) L’Ecuyer’s (1999) combined mul- tiple recursive generator (“le”), Matsumoto and Nishimura’s (1998) Mersenne Twister used in EViews 4 (“mt4”). btobs=integer Number of observations for bootstrap subsampling (when “bsmethod=pair”). Should be significantly greater than the number of regres- sors and less than or equal to the number of observations used in estimation. EViews will automatically restrict val- ues to the range from the number of regressors and the number of estimation observations. If omitted, the bootstrap will use the number of observa- tions used in estimation. btout=name (optional) Matrix to hold results of bootstrap simulations. k=arg (default=“e”) Kernel function for sparsity and coefficient covariance matrix estimation (when “spmethod=kernel”): “e” (Epan- echnikov), “r” (Triangular), “u” (Uniform), “n” (Normal– Gaussian), “b” (Biweight–Quartic), “t” (Triweight), “c” (Cosinus). m=integer Maximum number of iterations. s Use the current coefficient values in “C” as starting values (see also param (p. 453)). s=number (default =0) Determine starting values for equations. Specify a number between 0 and 1 representing the fraction of preliminary least squares coefficient estimates. Note that out of range values are set to the default. showopts / -showopts [Do / do not] display the starting coefficient values and estimation options in the estimation output.
- 465. 458—Chapter 12. Command Reference Examples qreg y c x estimates the default least absolute deviations (median) regression for the dependent vari- able Y on a constant and X. The estimates use the Huber Sandwich method for computing the covariance matrix, with individual sparsity estimates obtained using kernel methods. The bandwidth uses the Hall and Sheather formula. qreg(quant=0.6, cov=boot, btmethod=mcmba) y c x estimates the quantile regression for the 0.6 quantile using MCMB-A bootstrapping to obtain estimates of the coefficient covariance matrix. Cross-references See Chapter 8. “Quantile Regression,” on page 497 of the User’s Guide II for a discussion of the quantile regression. Reset the workfile range for a regular frequency workfile. No longer supported. See the replacement command pagestruct (p. 448). Import data from a foreign disk file into series. May be used to import data into an existing workfile from a text, Excel, or Lotus file on disk. Unless you need to merge data into an existing workfile page, we recommend that you use the more powerful, easy-to-use tools for reading data (see “Creating a Workfile by Reading from a Foreign Data Source” on page 47 of the User’s Guide I). See pageload (p. 440), and wfopen (p. 515) for command details. Syntax read(options) [path]file_name name1 [name2 name3 ...] read(options) [path]file_name n coef=arg Specify the name of the coefficient vector (if specified by list); the default behavior is to use the “C” coefficient vec- tor. prompt Force the dialog to appear from within a program. p Print estimation results. range Object Container, Data, and File Commands read Object Container, Data, and File Commands
- 466. read—459 You must supply the name of the source file. If you do not include the optional path specifi- cation, EViews will look for the file in the default directory. Path specifications may point to local or network drives. If the path specification contains a space, you may enclose the entire expression in double quotation marks. The input specification follows the source file name. There are two ways to specify the input series. First, you may list the names of the series in the order they appear in the file. Second, if the data file contains a header line for the series names, you may specify the number, n, of series in the file instead of a list of names. EViews will name the n series using the names given in the header line. If you specify a number and the data file does not contain a header line, EViews will name the series as SER01, SER02, SER03, and so on. To import data into alpha series, you must specify the names of your series, and should enter the tag “$” following the series name (e.g., “NAME $ INCOME CONSUMP”). Options File type options If you do not specify the “t” option, EViews uses the file name extension to determine the file type. If you specify the “t” option, the file name extension will not be used to determine the file type. Options for ASCII text files prompt Force the dialog to appear from within a program. t=dat, txt ASCII (plain text) files. t=wk1, wk3 Lotus spreadsheet files. t=xls Excel spreadsheet files. t Read data organized by series. Default is to read by obser- vation with series in columns. na=text Specify text for NAs. Default is “NA”. d=t Treat tab as delimiter (note: you may specify multiple delimiter options). The default is “d=c” only. d=c Treat comma as delimiter. d=s Treat space as delimiter. d=a Treat alpha numeric characters as delimiter. custom = symbol Specify symbol/character to treat as delimiter. mult Treat multiple delimiters as one.
- 467. 460—Chapter 12. Command Reference Options for spreadsheet (Lotus, Excel) files Examples read(t=dat,na=.) a:mydat.raw id lwage hrs reads data from an ASCII file MYDAT.RAW in the A: drive. The data in the file are listed by observation, the missing value NA is coded as a “.” (dot or period), and there are three series, which are to be named ID, LWAGE, HRS (from left to right). read(a2,s=sheet3) cps88.xls 10 reads data from an Excel file CPS88 in the default directory. The data are organized by obser- vation, the upper left data cell is A2, and 10 series are read from a sheet named SHEET3 using names provided in the file. read(a2, s=sheet2) "networkdr 1cps91.xls" 10 name Series names provided in file. label=integer Number of lines between the header line and the data. Must be used with the “name” option. rect (default) / norect [Treat / Do not treat] file layout as rectangular. skipcol = integer Number of columns to skip. Must be used with the “rect” option. skiprow = integer Number of rows to skip. Must be used with the “rect” option. comment= symbol Specify character/symbol to treat as comment sign. Every- thing to the right of the comment sign is ignored. Must be used with the “rect” option. singlequote Strings are in single quotes, not double quotes. dropstrings Do not treat strings as NA; simply drop them. negparen Treat numbers in parentheses as negative numbers. allowcomma Allow commas in numbers (note that using commas as a delimiter takes precedence over this option). currency= symbol Specify symbol/character for currency data. t Read data organized by series. Default is to read by obser- vation with series in columns. letter_number (default=“b2”) Coordinate of the upper-left cell containing data. s=sheet_name Sheet name for Excel 5–8 Workbooks.
- 468. reset—461 reads the Excel file CPS91 from the network drive specified in the path. Cross-references See “Importing Data” on page 139 of the User’s Guide I for a discussion and examples of importing data from external files. See also write (p. 535). Rename an object in the active workfile or database. Syntax rename old_name new_name [old_name1 new_name1 [old_name2 new_name2 [...]]] After the rename keyword, list the pairs of old object names followed by the new names. Note that the name specifications may include matching wildcard patterns. Examples rename temp_u u2 renames an object named TEMP_U as U2. rename aa::temp_u aa::u2 renames the object TEMP_U to U2 in database AA. rename a* b* renames all objects beginning with the letter “A” to begin with the letter “B”. rename a1 a2 b1 b2 renames A1 to A2 and B1 to B2. Cross-references See Chapter 4. “Object Basics,” on page 97 of the User’s Guide I for a discussion of working with objects in EViews. Compute Ramsey’s regression specification error test. Syntax reset(n, options) You must provide the number of powers of fitted terms n to include in the test regression. rename Object Utility Commands reset Interactive Use Commands
- 469. 462—Chapter 12. Command Reference Options Examples ls lwage c edu race gender reset(2) carries out the RESET test by including the square and the cube of the fitted values in the test equation. Cross-references See “Ramsey's RESET Test” on page 212 of the User’s Guide II for a discussion of the RESET test. Generate uniform random integers. The rndint command fills series, vector, and matrix objects with (pseudo) random integers drawn uniformly from zero to a user specified maximum. The rndint command ignores the current sample and fills the entire object with random integers. Syntax rndint(object_name, n) Type the name of the series, vector, or matrix object to fill, followed by an integer value rep- resenting the maximum value n of the random integers. n should a positive integer. Examples series index rndint(index,10) fills the entire series INDEX with integers drawn randomly from 0 to 10. Note that unlike standard series assignment using genr, rndint ignores the current sample and fills the series for the entire workfile range. sym(3) var3 rndint(var3,5) fills the entire symmetric matrix VAR3 with random integers ranging from 0 to 5. prompt Force the dialog to appear from within a program. p Print the test result. rndint Object Assignment Commands
- 470. rndseed—463 Cross-references See the list of available random number generators in “Statistical Distribution Functions” on page 569 of the User’s Guide I. See also nrnd (p. 606), rnd (p. 608) and rndseed (p. 463). Seed the random number generator. Use rndseed when you wish to generate a repeatable sequence of random numbers, or to select the generator to be used. Note that EViews 5 has updated the seeding routines of two of our pseudo-random number generators (backward compatible options are provided). It is strongly recommended that you use new generators. Syntax rndseed(options) integer Follow the rndseed keyword with the optional generator type and an integer for the seed. Options When EViews starts up, the default generator type is set to the improved Knuth lagged Fibo- nacci generator. Unless changed using rndseed, Knuth’s generator will be used for subse- quent pseudo-random number generation. rndseed Global Commands type=arg (default=“kn”) Type of random number generator: improved Knuth gener- ator (“kn”), improved Mersenne Twister (“mt”), Knuth’s (1997) lagged Fibonacci generator used in EViews 4 (“kn4)”, L’Ecuyer’s (1999) combined multiple recursive generator (“le”), Matsumoto and Nishimura’s (1998) Mersenne Twister used in EViews 4 (“mt4”). Knuth (“kn4”) L’Ecuyer (“le”) Mersenne Twister (“mt4”) Period Time (for draws) 27.3 secs 15.7 secs 1.76 secs Cases failed Diehard test 0 0 0 2 129 2 319 2 19937 1– 10 7
- 471. 464—Chapter 12. Command Reference Examples rndseed 123456 genr t3=@qtdist(rnd,3) rndseed 123456 genr t30=@qtdist(rnd,30) generates random draws from a t-distribution with 3 and 30 degrees of freedom using the same seed. Cross-references See the list of available random number generators in “Statistical Distribution Functions” on page 569 of the User’s Guide I. At press time, further information on the improved seeds may be found on the web at the following addresses: Knuth generator: http://sunburn.stanford.edu/~knuth/news02.html#rng Mersenne twister: http://www.math.keio.ac.jp/~matumoto/MT2002/emt19937ar.html See also nrnd (p. 606), rnd (p. 608) and rndint (p. 462). References Knuth, D. E. (1997). The Art of Computer Programming, Volume 2, Semi-numerical Algorithms, 3rd edition, Reading, MA: Addison-Wesley Publishing Company. Note: the C implementa- tion of the lagged Fibonacci generator is described in the errata to the 2nd edition, downloadable from Knuth's web site. L’Ecuyer, P. (1999). “Good Parameters and Implementations for Combined Multiple Recursive Ran- dom Number Generators,” Operations Research, 47(1), 159-164 Matsumoto, M. and T. Nishimura (1998). “Mersenne Twister: A 623-Dimensionally Equidistrib- uted Uniform Pseudo-Random Number Generator,” ACM Transactions on Modeling and Computer Simulation, 8(1), 3-30. Estimates an equation using robust least squares. You may perform three different types of robust estimation: M-estimation, S-estimation and MM-estimation. Syntax: robustls(options) y x1 [x2 x3…] Enter the robustls keyword, followed by the dependent variable and a list of the regres- sors. robustls Equation Methods
- 472. robustls—465 Options M-estimation Options method=arg (default=“m”) Robust estimation method: “m” (M-estimation), “s” (S-estimation) or “mm” (MM-estimation). cov=arg (default=“type1”) Covariance method type: “type1”, “type2”, or “type3”. tuning=number Specify a value for the tuning parameter. If a value is not specified, EViews will use the default tuning parameter for the type of estimation and weighting function (if applicable). c=s Convergence criterion. The criterion will be set to the nearest value between 1e-24 and 0.2. coef=arg Specify the name of the coefficient vector (if specified by list); the default behavior is to use the “C” coeffi- cient vector. m=integer Maximum number the number of iterations. prompt Force the dialog to appear from within a program. p Print results. fn=arg (default=“bisquare”) Weighting function used during M-estimation: “andrews” (Andrews), “bisquare” (Bisquare), “cau- chy” (Cauchy), “fair”, “huber”, “huberbi” (Huber- bisquare), “logistic” (Logistic), “median”, “tal” (Tal- worth), “Welsch” (Welsch). scale=arg (default=“madzero”) Scaling method used for calculating the scalar parame- ter during M estimation: “madzero” (median absolute deviation, zero centered), “madmed” (median abso- lute deviation, median centered), "huber" (Huber scal- ing). hmat Use the hat-matrix to down-weight observations with high leverage.
- 473. 466—Chapter 12. Command Reference S and MM estimation options MM estimation options Examples The following examples use the “Rousseeuw and Leroy.wf1” file located in the EViews application data directory. robustls salinity c lagsal trend discharge This line estimates a simple M-type robust estimation, with SALINITY as the dependent variable, and a constant, LAGSAL, TREND and DISCHARGE as independent variables. The line: robustls(method=mm, tuning=2.937, mtuning=3.44, cov=type2) salinity c lagsal trend discharge estimates the same model, but using MM-estimation, with an S tuning constant of 2.937, an M tuning constant of 3.44, and using Huber Type II standard errors. Cross-references compare = integer (default=4) Number of comparison sets. refine = integer (default= 2) Number of refinements. trials = integer (default=200) Number of trials. subsmpl=integer Specifies the size of the subsamples. Note, the default is number of coefficients in the regression. seed=number Specifies the random number generator seed rng=arg Specifies the type of random number generator. The key can be; improved Knuth generator (“kn”), improved Mersenne Twister (“mt”), Knuth’s (1997) lagged Fibonacci generator used in EViews 4 (“kn4”) L’Ecuyer’s (1999) combined multiple, recursive gener- ator (“le”), Matsumoto and Nishimura’s (1998) Mersenne Twister used in EViews 4 (“mt4”). mtuning=arg M-estimator tuning parameter. Note the S-estimator tuning parameter is set with the “tuning=” option outlined above. hmat Use the hat-matrix to down-weight observations with high leverage during m-estimation.
- 474. run—467 See Chapter 4. “Robust Least Squares,” beginning on page 405 of User’s Guide II for discus- sion. Run a program. The run command executes a program. The program may be located in memory or stored in a program file on disk. Syntax run(options) [path]prog_name(prog_options) [%0 %1 …] If you wish to pass one or more options to the program, you should enclose them in paren- theses immediately after the filename. If the program has arguments, you should list them after the filename. EViews first checks to see if the specified program is in memory. If the program is not located, EViews then looks for the program on disk in the current working directory, or in the specified path. The program file should have a “.PRG” extension, which you need not spec- ify in the prog_name. Options Examples run(q) simul(h=2) x xhat run Programming Commands integer (default=1) Set maximum errors allowed before halting the program. c Run program file without opening a window for display of the program file. verbose / quiet Verbose mode in which messages will be sent to the status line at the bottom of the EViews window (slower execu- tion), or quiet mode which suppresses workfile display updates (faster execution). v / q Same as [verbose / quiet]. ver4 / ver5 Execute program in [version 4 / version 5] compatibility mode. this=object_na me Set the _this object for the executed program. If omitted, the executed program will inherit the _this object from the parent program, or from the current active workfile object when the exec command is issued from the com- mand window.
- 475. 468—Chapter 12. Command Reference quietly runs a program named “Simul.prg” from the default directory using options string “h=2” and arguments %0=X and %1=XHAT. Since run is a command, it may also be placed in a program file. You should note that if you put the run command in a program file and then execute the program, EViews will stop after executing the program referred to by the run command. For example, if you have a pro- gram containing: run simul print x the print statement will not be executed since execution will stop after executing the com- mands in “Simul.prg”. If this behavior is not intended, you should consider using the exec (p. 350) command or an include (p. 713) statement. Cross-references See “Executing a Program” on page 120 of the User’s Guide I for further details. See also exec (p. 350) and include (p. 713). Save current workfile to disk. This usage is provided only for backward compatibility, as it has been replaced with the equivalent wfsave (p. 529) command. Syntax save [path]file_name Follow the keyword with a name for the file. If an explicit path is not specified, the file will be stored in the default directory, as set in the File Locations global options. Examples save MyWorkfile saves the current workfile with the name MYWORKFILE.WF1 in the default directory. save c:dataMyWF1 saves the current workfile with the name MYWF1.WF1 in the specified directory. Cross-references See wfsave (p. 529). save Object Container, Data, and File Commands
- 476. setcell—469 Seasonal adjustment. The seas command carries out seasonal adjustment using either the ratio to moving aver- age, or the difference from moving average technique. EViews also performs Census X11 and X12 seasonal adjustment. For details, see Series::x11 (p. 614) and Series::x12 (p. 616) in the Object Reference. Syntax seas(options) series_name name_adjust [name_fac] List the name of the original series and the name to be given to the seasonally adjusted series. You may optionally include an additional name for the seasonal factors. seas will display the seasonal factors using the convention of the Census X11 program. Options Examples seas(a) pass pass_adj pass_fac seasonally adjusts the series PASS using the additive method, and saves the adjusted series as PASS_ADJ and the seasonal factors as PASS_FAC. Cross-references See “Seasonal Adjustment” on page 418 of the User’s Guide I for a discussion of seasonal adjustment methods. See also seasplot (p. 948), Series::x11 (p. 614), and Series::x12 (p. 616) in the Object Reference. Insert contents into cell of a table. The setcell command puts a string or number into a cell of a table. seas Interactive Use Commands m Multiplicative (ratio to moving average) method. a Additive (difference from moving average) method. prompt Force the dialog to appear from within a program. setcell Table Commands
- 477. 470—Chapter 12. Command Reference Syntax setcell(table_name, r, c, content[, "options"]) Options Provide the following information in parentheses in the following order: the name of the table object, the row number, r, of the cell, the column number, c, of the cell, a number or string to put in the cell, and optionally, a justification and/or numerical format code. A string of text must be enclosed in double quotes. The justification options are: The numerical format code determines the format with which a number in a cell is dis- played; cells containing strings will be unaffected. The format code can either be a positive integer, in which case it specifies the number of decimal places to be displayed after the dec- imal point, or a negative integer, in which case it specifies the total number of characters to be used to display the number. These two cases correspond to the Fixed decimal and Fixed character fields in the number format dialog. Note that when using a negative format code, one character is always reserved at the start of a number to indicate its sign, and that if the number contains a decimal point, that will also be counted as a character. The remaining characters will be used to display digits. If the number is too large or too small to display in the available space, EViews will attempt to use scientific notation. If there is insufficient space for scientific notation (six characters or less), the cell will contain asterisks to indicate an error. Examples setcell(tab1, 2, 1, "Subtotal") puts the string “Subtotal” in row 2, column 1 of the table object named TAB1. setcell(tab1, 1, 1, "Price and cost", "r") puts the a right-justify string “Price and cost” in row 1, column 1 of the table object named TAB1. Note that in general, that unless you wish to control the formatting, assignment statements of the form Mytable(1,1) = "hello" are easier than using the default setcell statement: Setcell(mytable,1,1,"hello") c Center the text/number in the cell. r Right-justify the text/number in cell. l Left-justify the text/number in cell.
- 478. setcolwidth—471 Cross-references Chapter 3. “Working with Tables and Spreadsheets,” on page 57 describes table formatting using commands. See “Table Objects,” beginning on page 743 of the User’s Guide I for a dis- cussion and examples of table formatting in EViews. See also Table::setjust (p. 791) and Table::setformat (p. 786) in the Object Refer- ence. Note that this command is supported primarily for backward compatibility. There is a more extensive set of table procs for working with and customizing tables. See “Table Procs,” on page 770 in Object Reference. Set width of a column of a table. Provided for backward compatibility. See Table::setwidth (p. 797) in the Object Refer- ence for the new method of setting the width of table and spreadsheet columns. Syntax setcolwidth(table_name, c, width) Options To change the width of a column, provide the following information in parentheses, in the following order: the name of the table, the column number c, and the number of characters width for the new width. EViews measures units in terms of the width of a numeric charac- ter. Because different characters have different widths, the actual number of characters that will fit may differ slightly from the number you specify. By default, each column is approxi- mately 10 characters wide. Examples setcolwidth(mytab,2,20) sets the second column of table MYTAB to fit approximately 20 characters. Cross-references Chapter 3. “Working with Tables and Spreadsheets,” on page 57 of the User’s Guide I describes table formatting using commands. See also “Table Objects” on page 743 of the User’s Guide I for a discussion and examples of table formatting in EViews. Note that this command is supported primarily for backward compatibility. There is a more extensive set of table procs for working with and customizing tables. See “Table Procs,” on page 770 in the Object Reference. setcolwidth Table Commands
- 479. 472—Chapter 12. Command Reference See also Table::setwidth (p. 797) and Table::setheight (p. 790) in the Object Refer- ence. Place a double horizontal line in a table. Provided for backward compatibility. For a more general method of setting the line charac- teristics and borders for a set of table cells, see the table proc Table::setlines (p. 792) See also Table::setwidth (p. 797) and Table::setheight (p. 790) in the Object Refer- ence. Syntax setline(table_name, r) Options Specify the name of the table and the row number r in which to place the horizontal line. Examples setline(tab3,8) places a (double) horizontal line in the eighth row of the table object TAB3. Cross-references Chapter 3. “Working with Tables and Spreadsheets,” on page 57 of the User’s Guide I describes table formatting using commands. See also “Table Objects” on page 743 of the User’s Guide I for a discussion and examples of table formatting in EViews. See Table::setlines (p. 792) in the Object Reference for more flexible line drawing tools. Note that this command is supported primarily for backward compatibility. There is a more extensive set of table procs for working with and customizing tables. See “Table Procs,” on page 770 in the Object Reference. Start the Windows command shell, optionally executing a command. Syntax shell(options) [arg1 arg2 arg3…] See spawn (p. 480) for available options. By default, the Windows command shell will be started in hidden mode with the exit code for success set to zero. setline Table Commands shell Programming Commands
- 480. show—473 Examples shell mkdir c:newdir makes a new directory “c:newdir”. shell(out=flist) dir /b *.wf1 lists all workfiles in the current directory, saving output in a table named FLIST. Cross-references See spawn (p. 480) for details on spawning a new process. Display objects. The show command displays series or other objects on your screen. A scalar object is dis- played in the status line at the bottom of the EViews window. Syntax show object_name.view_command show object1 [object2 object3 ...] The command show should be followed by the name of an object, or an object name with an attached view. For series and graph objects, show can operate on a list of names. The list of names must be of the same type. show creates and displays an untitled group or multiple graph object. Examples genr x=nrnd show x.hist close x generates a series X of random draws from a standard normal distribution, displays the his- togram view of X, and closes the series window. show wage log(wage) opens an untitled group window with the spreadsheet view of the two series. freeze(gra1) wage.hist genr lwage=log(wage) freeze(gra2) lwage.hist show gra1 gra2 opens an untitled graph object with two histograms. show Command Actions
- 481. 474—Chapter 12. Command Reference Cross-references See “Object Commands” on page 17 for discussion. Chapter 1. “Object View and Procedure Reference,” on page 2 in the Object Reference pro- vides a complete listing of the views of the various objects. See also close (p. 310) and do (p. 349). Exponential smoothing. Forecasts a series using one of a number of exponential smoothing techniques. By default, smooth estimates the damping parameters of the smoothing model to minimize the sum of squared forecast errors, but you may specify your own values for the damping parameters. smooth automatically calculates in-sample forecast errors and puts them into the series RESID. Syntax smooth(method) series_name smooth_name [freq] You should follow the smooth keyword with the name of the series and a name for the smoothed series. You must also specify the smoothing method in parentheses. The optional freq may be used to override the default for the number of periods in the seasonal cycle. By default, this value is set to the workfile frequency (e.g. — 4 for quarterly data). For undated data, the default is 5. Options Smoothing method options smooth Interactive Use Commands s[,x] Single exponential smoothing for series with no trend. You may optionally specify a number x between zero and one for the mean parameter. d[,x] Double exponential smoothing for series with a trend. You may optionally specify a number x between zero and one for the mean parameter.
- 482. smooth—475 Other Options: If you wish to set only some of the damping parameters and let EViews estimate the other parameters, enter the letter “e” where you wish the parameter to be estimated. If the number of seasons is different from the frequency of the workfile (an unusual case that arises primarily if you are using an undated workfile for data that are not monthly or quarterly), you should enter the number of seasons after the smoothed series name. This optional input will have no effect on forecasts without seasonal components. Examples smooth(s) sales sales_f smooths the SALES series by a single exponential smoothing method and saves the smoothed series as SALES_F. EViews estimates the damping (smoothing) parameter and dis- plays it with other forecast statistics in the SALES series window. smooth(n,e,.3) tb3 tb3_hw smooths the TB3 series by a Holt-Winters no seasonal method and saves the smoothed series as TB3_HW. The mean damping parameter is estimated while the trend damping parameter is set to 0.3. smpl @first @last-10 smooth(m,.1,.1,.1) order order_hw smpl @all graph gra1.line order order_hw show gra1 n[,x,y] Holt-Winters without seasonal component. You may optionally specify numbers x and y between zero and one for the mean and trend parameters, respectively. a[,x,y,z] Holt-Winters with additive seasonal component. You may optionally specify numbers x, y, and z, between zero and one for the mean, trend, and seasonal parameters, respec- tively. m[,x,y,z] Holt-Winters with multiplicative seasonal component. You may optionally specify numbers x, y, and z, between zero and one for the mean, trend, and seasonal parameters, respectively. prompt Force the dialog to appear from within a program. p Print a table of forecast statistics.
- 483. 476—Chapter 12. Command Reference smooths the ORDER series by a Holt-Winters multiplicative seasonal method leaving the last 10 observations. The damping parameters are all set to 0.1. The last three lines plot and dis- play the actual and smoothed series over the full sample. Cross-references See “Exponential Smoothing” on page 466 of the User’s Guide I for a discussion of exponen- tial smoothing methods. Set sample range. The smpl command sets the workfile sample to use for statistical operations and series assignment expressions. Syntax smpl smpl_spec smpl sample_name List the date or number of the first observation and the date or number of the last observa- tion for the sample. Rules for specifying dates are given in “Dates” on page 94. The sample spec may contain more than one pair of beginning and ending observations. The smpl command also allows you to select observations on the basis of conditions speci- fied in an if statement. This enables you to use logical operators to specify what observa- tions to include in EViews’ procedures. Put the if statement after the pairs of dates. You can also use smpl to set the current observations to the contents of a named sample object; put the name of the sample object after the keyword. Special keywords for smpl The following “@-keywords” can be used in a smpl command: The following frequency functions may also be used: smpl Global Commands @all The entire workfile range. @first The first observation in the workfile. @last The last observation in the workfile.
- 484. smpl—477 In panel settings, you may use the additional keywords: Examples smpl 1955m1 1972m12 sets the workfile sample from 1955M1 to 1972M12. smpl @first 1940 1946 1972 1975 @last excludes observations (or years) 1941–1945 and 1973–1974 from the workfile sample. smpl if union=1 and edu<=15 sets the sample to those observations where UNION takes the value 1 and EDU is less than or equal to 15. sample half @first @first+@obs(x)/2 smpl half smpl if x>0 @year The four digit year in which the current observation begins. @quarter The quarter of the year in which the current observation begins. @month The month of the year in which the current observation begins. @day The day of the month in which the current observation begins. @weekday The day of the week in which the current observation begins, where Monday is given the number 1 and Sun- day is given the number 7. @hour The observation hour as an integer. (9:30AM returns 9, 5:15PM returns 17.) @minute The observation minute as an integer. (9:30PM returns 30.) @second The observation second as an integer. @hourf The observation time as a floating point hour. (9:30AM returns 9.5, 5:15PM returns 17.25.) @firstmin The earliest of the first observations (computed across cross-sections). @firstmax The latest of the first observations. @lastmin The earliest of the last observations. @lastmax The latest of the last observations.
- 485. 478—Chapter 12. Command Reference smpl @all if x>0 The first line declares a sample object named HALF which includes the first half of the series X. The second line sets the sample to HALF and the third line sets the sample to those obser- vations in HALF where X is positive. The last line sets the sample to those observations where X is positive over the full sample. The sample may be set for intraday data using optional times after the dates. For example, smpl 1/3/2000 10AM 12/30/2000 2PM removes any observations before 10AM on 1/3/2000 and after 2PM on 12/30/2000. smpl if @hourf<=9.5 and @hourf<=14.5 sets the sample to include only observations between and including 9:30AM and 2:30PM. smpl if @minute=0 or @minute=30 selects only observations that appear on the half hour. smpl if @weekday=1 and @hourf=10 sets the sample to include only observations that appear on Mondays at 10AM. Cross-references See “Samples” on page 129 of the User’s Guide I for a discussion of samples in EViews. See also Sample::set (p. 526) and Sample::sample (p. 525) in the Object Reference. Solve the model. solve finds the solution to a simultaneous equation model for the set of observations spec- ified in the current workfile sample. Syntax solve(options) model_name Note: when solve is used in a program (batch mode) models are always solved over the workfile sample. If the model contains a solution sample, it will be ignored in favor of the workfile sample. You should follow the name of the model after the solve command. The default solution method is dynamic simulation. You may modify the solution method as an option. solve Interactive Use Commands
- 486. sort—479 solve first looks for the specified model in the current workfile. If it is not present, solve attempts to fetch a model file (.DBL) from the default directory or, if provided, the path specified with the model name. Options solve can take any of the options available in Model::solveopt (p. 446) in the Object Ref- erence. Examples solve mod1 solves the model MOD1 using the default solution method. solve(m=500,e) nonlin2 solves the model NONLIN2 with an extended search of up to 500 iterations. Cross-references See Chapter 14. “Models,” on page 719 of the User’s Guide II for a discussion of models. See also Model::model (p. 435), Model::msg (p. 436) and Model::solveopt (p. 446) in the Object Reference. Sort the current workfile page. The sort command sorts all series in the workfile page on the basis of the values of one or more of the series. For purposes of sorting, NAs are considered to be smaller than any other value. By default, EViews will sort the series in ascending order. You may use options to override the sort order. EViews will first remove any workfile structures and then will sort the workfile using the specified settings. Syntax sort(options) arg1 [arg2 arg3…] List the name of the series or groups by which you wish to sort the workfile. If you list two or more series, sort uses the values of the second series to resolve ties from the first series, and values of the third series to resolve ties from the second, and so on. Options sort Object Container, Data, and File Commands d sort in descending order.
- 487. 480—Chapter 12. Command Reference Examples sort(d) inc sorts all series in the workfile in order of the INC series with the highest value of INC first. NAs in INC (if any) will be placed at the bottom. sort gender race wage sorts all series in the workfile in order of the values of GENDER from low to high, with ties resolved by ordering on the basis of RACE, with further ties resolved by ordering on the basis of WAGE. Cross-references See “Sorting a Workfile” on page 305 of the User’s Guide I. Spawn a new process. Syntax spawn(options) filename [arg1 arg2 arg3…] Follow the keyword with a filename indicating the process to spawn, and optional argu- ments to be passed to the process. Options spawn Programming Commands “n” or “normal” Create process in normal mode. The process will typically create a maximized window and may wait for user input. “m” or “minimized” Create process in a minimized window. Note that some applications may not accept requests to run in minimized mode. “h” or “hidden” Create process in hidden mode (without a visible window). Note that some applications may not accept requests to run in hidden mode.
- 488. stats—481 Examples spawn "c:program filesmicrosoft officeoffice11excel.exe" test.xls starts a new Excel process, passing it the command line argument “test.xls”. Cross-references See shell (p. 472) for information on starting a Windows command shell. Descriptive statistics. Computes and displays a table of means, medians, maximum and minimum values, stan- dard deviations, and other descriptive statistics of one or more series or a group of series. stats creates an untitled group containing all of the specified series, and opens a statistics view of the group. By default, if more than one series is given, the statistics are calculated for the common sample. Syntax stats(options) ser1 [ser2 ser3 …] t=isecs Specifies the maximum time in seconds that EViews should wait for the process to complete. If the timeout interval is reached and the process has not completed, EViews will generate an error. A timeout setting of zero may be used to indicate that EViews should not wait for the spawned pro- cess to complete. If no timeout option is provided, EViews will wait indefinitely for the process to complete. exit=icode Specifies the exit code that the process will return if it is completed successfully. If the process returns an exit code other than the specified value, EViews will generate an error. If the exit code option is not specified, EViews will not generate an error no matter what exit code is returned by the process. out = tablename If an output table name is specified, EViews will capture any data written to standard output by the spawned pro- cess and store it into a table object with the specified name in the workfile. Note that this option will have no effect unless either the minimized or hidden option is used and the timeout value is not zero. stats Interactive Use Commands
- 489. 482—Chapter 12. Command Reference Options Examples stats height weight age opens an untitled group window displaying the histogram and descriptive statistics for the common sample of the three series. Cross-references See “Descriptive Statistics & Tests” on page 376 and page 528 of the User’s Guide I for a dis- cussion of the descriptive statistics views of series and groups. See also boxplot (p. 907) and hist (p. 380). Send text to the status line. Displays a message in the status line at the bottom of the EViews main window. The mes- sage may include text, control variables, and string variables. Syntax statusline message_string Examples statusline Iteration Number: !t Displays the message “Iteration Number: !t” in the status line replacing “!t” with the current value of the control variable in the program. Cross-references See Chapter 6. “EViews Programming,” on page 117 of the User’s Guide I for a discussion and examples of programs, control variables and string variables. See also commandcap (p. 327). p Print the stats table. statusline Programming Commands
- 490. stepls—483 Estimation by stepwise least squares. Syntax stepls(options) y x1 [x2 x3 ...] @ z1 z2 z3 Specify the dependent variable followed by a list of variables to be included in the regres- sion, but not part of the search routine, followed by an “@” symbol and a list of variables to be part of the search routine. If no included variables are required, simply follow the depen- dent variable with an “@” symbol and the list of search variables. Options stepls Interactive Use Commands method = arg Stepwise regression method: “stepwise” (default), “uni” (uni-directional), “swap” (swapwise), “comb” (combinato- rial). nvars = int Set the number of search regressors. Required for swapwise and combinatorial methods, optional for uni-directional and stepwise methods. w=arg Weight series or expression. Note: we recommend that, absent a good reason, you employ the default settings Inverse std. dev. weights (“wtype=istdev”) with EViews default scaling (“wscale=eviews”) for backward compatibility with ver- sions prior to EViews 7. wtype=arg (default=“istdev”) Weight specification type: inverse standard deviation (“ist- dev”), inverse variance (“ivar”), standard deviation (“stdev”), variance (“var”). wscale=arg Weight scaling: EViews default (“eviews”), average (“avg”), none (“none”). The default setting depends upon the weight type: “eviews” if “wtype=istdev”, “avg” for all others. coef=arg Specify the name of the coefficient vector (if specified by list); the default behavior is to use the “C” coefficient vec- tor. prompt Force the dialog to appear from within a program. p Print estimation results.
- 491. 484—Chapter 12. Command Reference Stepwise and uni-directional method options Swapwise method options Combinatorial method options Examples stepls(method=comb,nvars=3) y c @ x1 x2 x3 x4 x5 x6 x7 x8 performs a combinatorial search routine to search for the three variables from the set of X1, X2, ..., X8, yielding the largest R-squared in a regression of Y on a constant and those three variables. Cross-references See “Stepwise Least Squares Regression,” beginning on page 49. Store objects in databases and databank files. Stores one or more objects in the current workfile in EViews databases or individual data- bank files on disk. The objects are stored under the name that appears in the workfile. back Set stepwise or uni-directional method to run backward. If omitted, the method runs forward. tstat Use t-statistic values as a stopping criterion. (Default uses p-values). ftol=number (default = 0.5) Set forward stopping criterion value. btol=number (default = 0.5) Set backward stopping criterion value. fmaxstep=int (default = 1000) Set the maximum number of steps forward. bmaxstep=int (default = 1000) Set the maximum number of steps backward. tmaxstep=int (default = 2000) Set the maximum total number of steps. minr2 Use minimum R-squared increments. (default uses maxi- mum R-squared increments.) force Suppress the warning message issued when a large number of regressions will be performed. store Object Container, Data, and File Commands
- 492. store—485 Syntax store(options) object_list Follow the store command keyword with a list of object names (each separated by a space) that you wish to store. The default is to store the objects in the default database. (This behavior is a change from EViews 2 and earlier where the default was to store objects in indi- vidual databank files). You may precede the object name with a database name and the double colon “::” to indi- cate a specific database. You can also specify the database name as an option in parenthe- ses, in which case all objects without an explicit database name will be stored in the specified database. You may use wild card characters “?” (to match any single character) or “*” (to match zero or more characters) in the object name list. All objects with names matching the pattern will be stored. You can optionally choose to store the listed objects in individual databank files. To store in files other than the default path, you should include a path designation before the object name. Options If you do not specify the precision option (1 or 2), the global option setting will be used. See “Database Storage Defaults” on page 823 of the User’s Guide II. Examples store m1 gdp unemp stores the three objects M1, GDP, UNEMP in the default database. store(d=us1) m1 gdp macro::unemp stores M1 and GDP in the US1 database and UNEMP in the MACRO database. store usdat::gdp macro::gdp d=db_name Store to the specified database. i Store to individual databank files. 1 / 2 Store series in [single / double] precision to save space. o Overwrite object in database (default is to merge data, where possible). g=arg Group store from workfile to database: “s” (copy group definition and series as separate objects), “t” (copy group definition and series as one object), “d” (copy series only as separate objects), “l” (copy group definition only).
- 493. 486—Chapter 12. Command Reference stores the same object GDP in two different databases USDAT and MACRO. store(1) cons* stores all objects with names starting with CONS in the default database. The “1” option uses single precision to save space. store(i) m1 c:dataunemp stores M1 and UNEMP in individual databank files. Cross-references “Basic Data Handling” on page 119 of the User’s Guide I discusses exporting data in other file formats. See Chapter 10. “EViews Databases,” on page 307 of the User’s Guide I for a dis- cussion of EViews databases and databank files. For additional discussion of wildcards, see Appendix A. “Wildcards,” on page 739 of the User’s Guide I. See also fetch (p. 355) and copy (p. 327). Estimate a switching regression model (simple exogenous or Markov). Syntax switchreg(options) dependent_var list_of_varying_regressors [ @nv list_of_nonvary- ing_regressors ] [ @prv list_of_probability_regressors ] List the switchreg keyword, followed by options, then the dependent variable and a list of the regressors with regime-varying coefficients, following optionally by the keyword @nv and a list of regressors with regime-invariant coefficients, and by the keyword @prv and a list of regressors that enter into the transition probability specification. The dependent variable in switchreg may not be an expression. Dynamics may be speci- fied by including lags of the dependent variable as regressors, or by specifying AR errors using the AR keyword. The latter incorporate mean adjusted lags of the form specified by the “Hamilton-model.” Options switchreg Interactive Use Commands type=arg Type of switching: simple exogenous (“simple”), Markov (“markov”). nstates=integer (default=2) Number of regimes.
- 494. switchreg—487 In addition to the specification options, there are options for estimation and covariance cal- culation. heterr Allow for heterogeneous error variances across regimes fprobmat=arg Name of fixed transition probability matrix allows for fixing specific elements of the time-invariant transition matrix. Leave NAs in elements of the matrix to estimate. The element of the matrix corresponds to . initprob=arg (default=“ergodic”) Method for determining initial Markov regime probabilities: ergodic solution (“ergodic”), estimated parameter (“est”), equal probabilities (“uniform”), user-specified probabilities (“user”). If “initprob=user” is specified, you will need to specify the “userinit=” option. userinit=arg Name of vector containing user-specified initial Markov probabilities. The vector should have rows equal to the number of states; we expand this to the size of the initial lag state vector where necessary for AR specifications. For use in specifications containing both the “type=mar- kov” and “initprob=user” options. startnum=arg (default=0 or 25) Number of random starting values tried. The default is 0 for user-supplied coefficients (option “s”) and 25 in all other cases. startiter=arg (default=10) Number of iterations taken after each random start before comparing objective to determine final starting value. searchnum=arg (default=0) Number of post-estimation perturbed starting values tried. searchstds=arg (default=1) Number of standard deviations to use in perturbed starts (if “searchnum=”) is specified. seed=positive_inte- ger from 0 to 2,147,483,647 Seed the random number generator. If not specified, EViews will seed random number genera- tor with a single integer draw from the default global ran- dom number generator. rnd=arg (default=“kn” or method previously set using rndseed (p. 463) in the Com- mand and Program- ming Reference). Type of random number generator: improved Knuth gener- ator (“kn”), improved Mersenne Twister (“mt”), Knuth’s (1997) lagged Fibonacci generator used in EViews 4 (“kn4”) L’Ecuyer’s (1999) combined multiple recursive generator (“le”), Matsumoto and Nishimura’s (1998) Mersenne Twister used in EViews 4 (“mt4”). i j, P st j st 1– i
- 495. 488—Chapter 12. Command Reference Additional Options Examples switchreg(type=markov) y c @nv ar(1) ar(2) ar(3) ar(4) optmethod = arg Optimization method: “bfgs” (BFGS); “newton” (Newton- Raphson), “opg” or “bhhh” (OPG or BHHH), “legacy” (EViews legacy). BFGS is the default method. optstep = arg Step method: “marquardt” (Marquardt); “dogleg” (Dog- leg); “linesearch” (Line search). Marquardt is the default method. m=integer Set maximum number of iterations. c=scalar Set convergence criterion. The criterion is based upon the maximum of the percentage changes in the scaled coeffi- cients. The criterion will be set to the nearest value between 1e-24 and 0.2. cov=arg Covariance method: “ordinary” (default method based on inverse of the estimated information matrix), “huber” or “white” (Huber-White sandwich method). covinfo = arg Information matrix method: “opg” (OPG); “hessian” (observed Hessian). (Applicable when non-legacy “optmethod=”.) nodf Do not degree-of-freedom correct the coefficient covariance estimate. coef=arg Specify the name of the coefficient vector (if specified by list); the default behavior is to use the “C” coefficient vec- tor. s Use the current coefficient values in “C” as starting values (see also param (p. 453) of the Command and Program- ming Reference). s=number Specify a number between zero and one to determine start- ing values as a fraction of EViews default values (out of range values are set to “s=1”). showopts / -showopts [Do / do not] display the starting coefficient values and estimation options in the estimation output. prompt Force the dialog to appear from within a program. p Print results.
- 496. tabplace—489 estimates a Hamilton-type Markov switching regression model with four non-regime varying autoregressive terms implying mean adjustment for the lagged endogenous. switchreg(type=markov) y c @nv y(-1) y(-2) y(-3) y(-4) specifies an alternate dynamic model in which the lags enter directly into the contemporane- ous equation without mean adjustment. switchreg(type=markov) yy_dalt c @nv ar(1) ar(2) ar(3) ar(4) @prv c yy_ldalt estimates a 2 state model with non-varying AR(4) and transition matrix probability regressor YY_LDALT. Cross-references See Chapter 7. “Switching Regression,” beginning on page 461 of User’s Guide II for a description of the switching regression methodology. See also Equation::rgmprobs (p. 152), Equation::transprobs (p. 167), Equa- tion::makergmprobs (p. 128) and Equation::maketransprobs (p. 129), all in the Object Reference, for routines that allow you to work with the regime probabilities and tran- sition probabilities. Copies a portion of one table to the specified location in another table. Syntax tabplace(desttable,sourcetable,d1,s1,s2) tabplace(desttable,sourcetable,dr1,dc1,sr1,sc1,sr2,sc2) The tabplace command can be specified either using coordinates where columns are signi- fied with a letter, and rows by a number (for example “A3” represents the first column, third row), or by row number and column number. The first syntax represents coordinate form, where sourcetable is the name of the table from which to copy, s1 specifies the upper-left coordinate portion of the section of the source table to be copied, s2 specifies the bottom-right coordinate, desttable specifies the name of the table to copy to, and d1 specifies the upper-left coordinate of the destination table. The second syntax represents the row/column number form, where sourcetable is the name of the table from which to copy, sr1 specifies the source table upper row number, sc1 speci- fies the source table left most column number, sr2 specifies the source table bottom row number, sc2 specifies the source table right most column number. desttable specifies the tabplace Table Commands
- 497. 490—Chapter 12. Command Reference name of the table to copy to, and dr1 and dr2 specify the upper and left most row and col- umn of the destination table, respectively. Examples tabplace(table2,table1,"d1","B9","E17") places a copy of the data from cell range B9 to E17 in TABLE1 to TABLE2 at cell D1 tabplace(table3,table1,10,3,9,2,17,5) copies 8 rows of data (from row 9 to row 17) and 3 columns (from 2 to 5)of data in TABLE1 to the tenth row and 3rd column of TABLE3. Cross-references See also Table::copytable (p. 775). For additional discussion of table commands see Chapter 3. “Working with Tables and Spreadsheets,” on page 57 of the Command and Programming Reference. See also Chapter 16. “Table and Text Objects,” on page 743 of the User’s Guide I for a discus- sion and examples of table formatting in EViews. Test whether to add regressors to an estimated equation. Tests the hypothesis that the listed variables were incorrectly omitted from an estimated equation (only available for equations estimated by list). The test displays some combina- tion of Wald and LR test statistics, as well as the auxiliary regression. Syntax testadd(options) arg1 [arg2 arg3 ...] List the names of the series or groups of series to test for omission after the keyword. The test is applied to the default equation, if defined. Options Examples ls sales c adver lsales ar(1) testadd gdp gdp(-1) tests whether GDP and GDP(-1) belong in the specification for SALES. The commands: testadd Interactive Use Commands prompt Force the dialog to appear from within a program. p Print output from the test.
- 498. threshold—491 Cross-references See “Coefficient Diagnostics” on page 164 of the User’s Guide II for further discussion. See also testdrop (p. 491). Test whether to drop regressors from a regression. Tests the hypothesis that the listed variables were incorrectly included in the estimated equation (only available for equations estimated by list). The test displays some combina- tion of and LR test statistics, as well as the test regression. Syntax testdrop(options) arg1 [arg2 arg3 ...] List the names of the series or groups of series to test for omission after the keyword. The test is applied to the default equation, if defined. Options Examples ls sales c adver lsales ar(1) testdrop adver tests whether ADVER should be excluded from the specification for SALES. The commands: Cross-references See “Coefficient Diagnostics” on page 164 of the User’s Guide II for further discussion of testing coefficients. See also testadd (p. 490) and Equation::wald (p. 175) in the Object Reference. Estimation by threshold least squares, including threshold autoregression. Syntax threshold(options) y z1 [z2 z3 ...] [@nv x1 x2 x3 ...] @thresh t1 [t2 t3 ...] testdrop Interactive Use Commands prompt Force the dialog to appear from within a program. p Print output from the test. threshold Interactive Use Commands F
- 499. 492—Chapter 12. Command Reference List the dependent variable first, followed by a list of the independent variables that have coefficients that are allowed to vary across threshold, followed optionally by the keyword @nv and a list of non-varying coefficient variables. List a threshold variable or variables (for model selection) or a single integer or range pairs after the keyword @thresh. The integer or range pairs indicate a self-exciting model with the lagged dependent variable as the threshold variable. Options Threshold Options method=arg (default=“seqplus1”) Threshold selection method: “seqplus1” (sequential tests of single versus thresholds), “seqall” (sequential test of all possible versus thresh- olds), “glob” (tests of global vs. no thresholds), “glob- plus1” (tests of versus globally determined thresholds), “globinfo” (information criteria evalua- tion)., “fixedseq” (fixed number of sequentially deter- mined thresholds), “fixedglob” (fixed number of globally determined thresholds), “user” (user-specified thresholds) nthresh=arg (default=1) Number of thresholds for fixed number threshold selec- tion methods. select=arg Sub-method setting (options depend on “method=”). (1) if “method=glob”: Sequential ("seq") (default), Highest significant ("high"), ("udmax"), ("wdmax"). (2) if “method=globinfo”: Schwarz criterion (“bic” or “sic”) (default), Liu-Wu-Zidek criterion (“lwz”). trim=arg (default=5) Trimming percentage for determining minimum segment size (5, 10, 15, 20, 25). maxthresh=integer (default=5) Maximum number of thresholds to allow (not applicable if “method=seqall”). maxlevels=integer (default=5) Maximum number of threshold levels to consider in sequential testing (applicable when “method=sequall”). breaks=arg User-specified threshold values. For use when “method=user”. l 1 l l 1 l l l 1 l UDmax WDmax
- 500. threshold—493 General Options size=arg (default=5) Test sizes for use in sequential determination and final test evaluation (10, 5, 2.5, 1) corresponding to 0.10, 0.05, 0.025, 0.01, respectively heterr Assume regimes specific error distributions in variance computation. commondata Assume a common distribution for the data across seg- ments (only applicable if original equation is estimated with a robust covariance method, “heterr” is not speci- fied). w=arg Weight series or expression. wtype=arg (default=“istdev”) Weight specification type: inverse standard deviation (“ist- dev”), inverse variance (“ivar”), standard deviation (“stdev”), variance (“var”). wscale=arg Weight scaling: EViews default (“eviews”), average (“avg”), none (“none”). The default setting depends upon the weight type: “eviews” if “wtype=istdev”, “avg” for all others. cov=keyword Covariance type (optional): “white” (White diagonal matrix), “hac” (Newey-West HAC). nodf Do not perform degree of freedom corrections in computing coefficient covariance matrix. The default is to use degree of freedom corrections. covlag=arg (default=1) Whitening lag specification: integer (user-specified lag value), “a” (automatic selection). covinfosel=arg (default=“aic”) Information criterion for automatic selection: “aic” (Akaike), “sic” (Schwarz), “hqc” (Hannan-Quinn) (if “lag=a”). covmaxlag=integer Maximum lag-length for automatic selection (optional) (if “lag=a”). The default is an observation-based maximum of . covkern=arg (default=“bart”) Kernel shape: “none” (no kernel), “bart” (Bartlett, default), “bohman” (Bohman), “daniell” (Daniel), “parzen” (Parzen), “parzriesz” (Parzen-Riesz), “parzgeo” (Parzen- Geometric), “parzcauchy” (Parzen-Cauchy), “quadspec” (Quadratic Spectral), “trunc” (Truncated), “thamm” (Tukey-Hamming), “thann” (Tukey-Hanning), “tparz” (Tukey-Parzen). T 1 3
- 501. 494—Chapter 12. Command Reference Examples threshold(method=fixedseq) ss_transf c ss_transf(-1 to -11) @thresh 2 uses the fixed number of thresholds test to determine the optimal threshold in a model regressing SS_TRANSF on the threshold variables C and SS_TRANSF(-1 to -11). threshold(method=fixedseq) ss_transf c ss_transf(-1 to -11) @thresh 1 5 uses the fixed number of thresholds test to determine the optimal threshold and does model selection over lags of SS_TRANSF from SS_TRANSF(-1) to SS_TRANSF(-5). threshold(method=user, threshold=7.44) ss_transf c @nv ss_transf( -1 to -11) @thresh 2 estimates the model with one user-specified threshold value. In addition, the variables SS_TRANSF(-1 to -11) are restricted to have common coefficients across the regimes. Cross-references See “Threshold Regression” on page 445 for a discussion of the various forms of threshold models. Reset the timer. Syntax Command: tic Examples The sequence of commands: tic covbw=arg (default=“fixednw” ) Kernel Bandwidth: “fixednw” (Newey-West fixed), “andrews” (Andrews automatic), “neweywest” (Newey- West automatic), number (User-specified bandwidth). covnwlag=integer Newey-West lag-selection parameter for use in nonpara- metric kernel bandwidth selection (if “covbw=newey- west”). covbwint Use integer portion of bandwidth. prompt Force the dialog to appear from within a program. p Print basic estimation results. tic Programming Commands
- 502. toc—495 [some commands] toc resets the timer, executes commands, and then displays the elapsed time in the status line. Alternatively: tic [some commands] !elapsed = @toc resets the time, executes commands, and saves the elapsed time in the control variable !ELAPSED. Cross-references See also toc (p. 495) and @toc (p. 726). Display elapsed time (since timer reset) in seconds. Syntax Command: toc Examples The sequence of commands: tic [some commands] toc resets the timer, executes commands, and then displays the elapsed time in the status line. The set of commands: tic [some commands] !elapsed = @toc [more commands] toc resets the time, executes commands, saves the elapsed time in the control variable !ELAPSED, executes additional commands, and displays the total elapsed time in the status line. Cross-references See also tic (p. 494) and @toc (p. 726). toc Programming Commands
- 503. 496—Chapter 12. Command Reference Two-stage least squares. Carries out estimation using two-stage least squares. Syntax tsls(options) y x1 [x2 x3 ...] @ z1 [z2 z3 ...] tsls(options) specification @ z1 [z2 z3 ...] To use the tsls command, list the dependent variable first, followed by the regressors, then any AR or MA error specifications, then an “@”-sign, and finally, a list of exogenous instru- ments. You may estimate nonlinear equations or equations specified with formulas by first providing a specification, then listing the instrumental variables after an “@”-sign. There must be at least as many instrumental variables as there are independent variables. All exogenous variables included in the regressor list should also be included in the instru- ment list. A constant is included in the list of instrumental variables even if not explicitly specified. Options Non-Panel TSLS Options tsls Interactive Use Commands nocinst Do not automatically include a constant as an instrument. w=arg Weight series or expression. Note: we recommend that, absent a good reason, you employ the default settings Inverse std. dev. weights (“wtype=istdev”) with EViews default scaling (“wscale=eviews”) for backward compatibility with ver- sions prior to EViews 7. wtype=arg (default=“istdev”) Weight specification type: inverse standard deviation (“ist- dev”), inverse variance (“ivar”), standard deviation (“stdev”), variance (“var”). wscale=arg Weight scaling: EViews default (“eviews”), average (“avg”), none (“none”). The default setting depends upon the weight type: “eviews” if “wtype=istdev”, “avg” for all others. z Turn off backcasting in ARMA models. cov=keyword Covariance type (optional): “white” (White diagonal matrix), “hac” (Newey-West HAC).
- 504. tsls—497 nodf Do not perform degree of freedom corrections in computing coefficient covariance matrix. The default is to use degree of freedom corrections. covlag=arg (default=1) Whitening lag specification: integer (user-specified lag value), “a” (automatic selection). covinfosel=arg (default=“aic”) Information criterion for automatic selection: “aic” (Akaike), “sic” (Schwarz), “hqc” (Hannan-Quinn) (if “lag=a”). covmaxlag=integer Maximum lag-length for automatic selection (optional) (if “lag=a”). The default is an observation-based maximum of . covkern=arg (default=“bart”) Kernel shape: “none” (no kernel), “bart” (Bartlett, default), “bohman” (Bohman), “daniell” (Daniel), “parzen” (Parzen), “parzriesz” (Parzen-Riesz), “parzgeo” (Parzen- Geometric), “parzcauchy” (Parzen-Cauchy), “quadspec” (Quadratic Spectral), “trunc” (Truncated), “thamm” (Tukey-Hamming), “thann” (Tukey-Hanning), “tparz” (Tukey-Parzen). covbw=arg (default=“fixednw” ) Kernel Bandwidth: “fixednw” (Newey-West fixed), “andrews” (Andrews automatic), “neweywest” (Newey- West automatic), number (User-specified bandwidth). covnwlag=integer Newey-West lag-selection parameter for use in nonpara- metric kernel bandwidth selection (if “covbw=newey- west”). covbwint Use integer portion of bandwidth. m=integer Set maximum number of iterations. c=scalar Set convergence criterion. The criterion is based upon the maximum of the percentage changes in the scaled coeffi- cients. The criterion will be set to the nearest value between 1e-24 and 0.2. s Use the current coefficient values in estimator coefficient vector as starting values for equations specified by list with AR or MA terms (see also param (p. 453) of the Com- mand and Programming Reference). T 1 3
- 505. 498—Chapter 12. Command Reference Panel TSLS Options s=number Determine starting values for equations specified by list with AR or MA terms. Specify a number between zero and one representing the fraction of TSLS estimates computed without AR or MA terms to be used. Note that out of range values are set to “s=1”. Specifying “s=0” initializes coeffi- cients to zero. By default EViews uses “s=1”. Does not apply to coefficients for AR and MA terms which are set to EViews determined default values. numericderiv / -numericderiv [Do / do not] use numeric derivatives only. If omitted, EViews will follow the global default. fastderiv / -fastderiv [Do / do not] use fast derivative computation. If omitted, EViews will follow the global default. showopts / -showopts [Do / do not] display the starting coefficient values and estimation options in the estimation output. coef=arg Specify the name of the coefficient vector (if specified by list); the default behavior is to use the “C” coefficient vec- tor. prompt Force the dialog to appear from within a program. p Print basic estimation results. cx=arg Cross-section effects. For fixed effects estimation, use “cx=f”; for random effects estimation, use “cx=r”. per=arg Period effects. For fixed effects estimation, use “cx=f”; for random effects estimation, use “cx=r”. wgt=arg GLS weighting: (default) none, cross-section system weights (“wgt=cxsur”), period system weights (“wgt=persur”), cross-section diagonal weighs (“wgt=cxdiag”), period diagonal weights (“wgt=per- diag”). cov=arg Coefficient covariance method: (default) ordinary, White cross-section system robust (“cov=cxwhite”), White period system robust (“cov=perwhite”), White heteroske- dasticity robust (“cov=stackedwhite”), Cross-section sys- tem robust/PCSE (“cov=cxsur”), Period system robust/PCSE (“cov=persur”), Cross-section heteroskedas- ticity robust/PCSE (“cov=cxdiag”), Period heteroskedastic- ity robust (“cov=perdiag”). keepwgts Keep full set of GLS weights used in estimation with object, if applicable (by default, only small memory weights are saved).
- 506. tsls—499 rancalc=arg (default=“sa”) Random component method: Swamy-Arora (“ran- calc=sa”), Wansbeek-Kapteyn (“rancalc=wk”), Wallace- Hussain (“rancalc=wh”). nodf Do not perform degree of freedom corrections in computing coefficient covariance matrix. The default is to use degree of freedom corrections. iter=arg (default=“onec”) Iteration control for GLS specifications: perform one weight iteration, then iterate coefficients to convergence (“iter=onec”), iterate weights and coefficients simultane- ously to convergence (“iter=sim”), iterate weights and coefficients sequentially to convergence (“iter=seq”), per- form one weight iteration, then one coefficient step (“iter=oneb”). Note that random effects models currently do not permit weight iteration to convergence. unbalsur Compute SUR factorization in unbalanced data using the subset of available observations for a cluster. coef=arg Specify the name of the coefficient vector (if specified by list); the default is to use the “C” coefficient vector. s Use the current coefficient values in estimator coefficient vector as starting values for equations specified by list with AR terms (see also param (p. 453) of the Command and Programming Reference). s=number Determine starting values for equations specified by list with AR terms. Specify a number between zero and one representing the fraction of TSLS estimates computed with- out AR terms to be used. Note that out of range values are set to “s=1”. Specifying “s=0” initializes coefficients to zero. By default EViews uses “s=1”. Does not apply to coefficients for AR terms which are instead set to EViews determined default values. m=integer Set maximum number of iterations. c=number Set convergence criterion. The criterion is based upon the maximum of the percentage changes in the scaled coeffi- cients. The criterion will be set to the nearest value between 1e-24 and 0.2. numericderiv / -numericderiv [Do / do not] use numeric derivatives only. If omitted, EViews will follow the global default. fastderiv / -fastderiv [Do / do not] use fast derivative computation. If omitted, EViews will follow the global default.
- 507. 500—Chapter 12. Command Reference Examples tsls y_d c cpi inc ar(1) @ lw(-1 to -3) estimates an UNTITLED equation using TSLS regression of Y_D on a constant, CPI, INC with AR(1) using a constant, LW(-1), LW(-2), and LW(-3) as instruments. param c(1) .1 c(2) .1 tsls(s,m=500) y_d=c(1)+inc^c(2) @ cpi estimates a nonlinear TSLS model using a constant and CPI as instruments. The first line sets the starting values for the nonlinear iteration algorithm. Cross-references See Chapter 20. “Additional Regression Tools,” on page 23 and “Two-Stage Least Squares” on page 603 of the User’s Guide II for details on two-stage least squares estimation in single equations and systems, respectively. “Instrumental Variables” on page 824 of the User’s Guide II discusses estimation using pool objects, while “Instrumental Variables Estimation” on page 858 of the User’s Guide II discusses estimation in panel structured workfiles. See also ls (p. 410). Andrews-Quandt test for unknown breakpoint. Carries out the Andrews-Quandt test for parameter stability at some unknown breakpoint. Syntax ubreak(options) trimlevel @ x1 x2 x3 You must provide the level of trimming of the data. The level must be one of the following: 49, 48, 47, 45, 40, 35, 30, 25, 20, 15, 10, or 5. If the equation is specified by list and contains no linear terms, you may specify a subset of the regressors to be tested for a breakpoint after an “@” sign. showopts / -showopts [Do / do not] display the starting coefficient values and estimation options in the estimation output. prompt Force the dialog to appear from within a program. p Print estimation results. ubreak Interactive Use Commands
- 508. unlink—501 Options Examples ls log(spot) c log(p_us) log(p_uk) ubreak 15 regresses the log of SPOT on a constant, the log of P_US, and the log of P_UK, and then car- ries out the Andrews-Quandt test, trimming 15% of the data from each end. To test whether only the constant term and the coefficient on the log of P_US are subject to a structural break, use: ubreak @ c log(p_us) Cross-references See “Quandt-Andrews Breakpoint Test” on page 196 of the User’s Guide II for further discus- sion. See also Equation::chow (p. 60) and Equation::rls (p. 153) in the Object Reference. Break links and auto-updating series (formulae) in the specified series objects. Syntax unlink link_names unlink converts link objects and auto-updating series to ordinary series or alphas. Follow the keyword with a list of names of links and auto-updating series to be converted to ordi- nary series (values). The list of links may include wildcard characters. Examples unlink gdp income converts the link series GDP and INCOME to ordinary series. unlink * wfname = series_name Store the individual Wald F-statistics into the series series_name. lfname = series_name Store the individual likelihood ratio F-statistics into the series series_name. prompt Force the dialog to appear from within a program. p Print the result of the test. unlink Object Container, Data, and File Commands
- 509. 502—Chapter 12. Command Reference breaks all links in the active workfile page. Cross-references See Chapter 8. “Series Links,” on page 223 of the User’s Guide I for a description of link objects, and “Auto-Updating Series” on page 193 of the User’s Guide I for a discussion of auto-updating series. See also Link::link (p. 352) and Link::linkto (p. 353). See also pageunlink (p. 450) and wfunlink (p. 533) for page and workfile based unlink- ing, respectively. Carries out unit root tests on a single series, pool series, group of series, or panel structured series. The ordinary, single series unit root tests include Augmented Dickey-Fuller (ADF), GLS detrended Dickey-Fuller (DFGLS), Phillips-Perron (PP), Kwiatkowski, et. al. (KPSS), Elliot, Rothenberg, and Stock (ERS) Point Optimal, or Ng and Perron (NP) tests for a unit root in the series (or its first or second difference). If used on a series in a panel structured workfile, or with a pool series, or group of series, the procedure will perform panel unit root testing. The panel unit root tests include Levin, Lin and Chu (LLC), Breitung, Im, Pesaran, and Shin (IPS), Fisher - ADF, Fisher - PP, and Hadri tests on levels, or first or second differences. Syntax uroot(options) object_name where object_name can be the name of a series, group or pool object. Options Basic Specification Options You should specify the exogenous variables and order of dependent variable differencing in the test equation using the following options: uroot Interactive Use Commands const (default) Include a constant in the test equation. trend Include a constant and a linear time trend in the test equa- tion. none Do not include a constant or time trend (only available for the ADF and PP tests). dif=integer (default=0) Order of differencing of the series prior to running the test. Valid values are {0, 1, 2}.
- 510. uroot—503 For backward compatibility, the shortened forms of these options, “c”, “t”, and “n”, are presently supported. For future compatibility we recommend that you use the longer forms. For ordinary (non-panel) unit root tests, you should specify the test type using one of the following keywords: For panel testing, you may use one of the following keywords to specify the test: Options for ordinary (non-panel) unit root tests In addition, the following panel specific options are available: adf (default) Augmented Dickey-Fuller. dfgls GLS detrended Dickey-Fuller (Elliot, Rothenberg, and Stock). pp Phillips-Perron. kpss Kwiatkowski, Phillips, Schmidt, and Shin. ers Elliot, Rothenberg, and Stock (Point Optimal). np Ng and Perron. sum (default) Summary of all of the panel unit root tests. llc Levin, Lin, and Chu. breit Breitung. ips Im, Pesaran, and Shin. adf Fisher - ADF. pp Fisher - PP. hadri Hadri. hac=arg Method of estimating the frequency zero spectrum: “bt” (Bartlett kernel), “pr” (Parzen kernel), “qs” (Quadratic Spectral kernel), “ar” (AR spectral), “ardt (AR spectral - OLS detrended data), “argls” (AR spectral - GLS detrended data). Applicable to PP, KPSS, ERS, and NP tests. The default set- tings are test specific (“bt” for PP and KPSS, “ar” for ERS, “argls” for NP).
- 511. 504—Chapter 12. Command Reference Options for panel unit root tests The following panel specific options are available: band = arg, b=arg (default=“nw”) Method of selecting the bandwidth: “nw” (Newey-West automatic variable bandwidth selection), “a” (Andrews automatic selection), number (user specified bandwidth). Applicable to PP, KPSS, ERS, and NP tests when using ker- nel sums-of-covariances estimators (where “hac=” is one of {bt, pz, qs}). lag=arg (default=“a”) Method of selecting lag length (number of first difference terms) to be included in the regression: “a” (automatic information criterion based selection), or integer (user- specified lag length). Applicable to ADF and DFGLS tests, and for the other tests when using AR spectral density estimators (where “hac=” is one of {ar, ardt, argls}). infosel=arg (default=“sic”) Information criterion to use when computing automatic lag length selection: “aic” (Akaike), “sic” (Schwarz), “hqc” (Hannan-Quinn), “msaic” (Modified Akaike), “msic” (Modified Schwarz), “mhqc” (Modified Hannan-Quinn). Applicable to ADF and DFGLS tests, and for other tests when using AR spectral density estimators (where “hac=” is one of {ar, ardt, argls}). maxlag=integer Maximum lag length to consider when performing auto- matic lag length selection: default= Applicable to ADF and DFGLS tests, and for other tests when using AR spectral density estimators (where “hac=” is one of {ar, ardt, argls}). balance Use balanced (across cross-sections or series) data when performing test. hac=arg (default=“bt”) Method of estimating the frequency zero spectrum: “bt” (Bartlett kernel), “pr” (Parzen kernel), “qs” (Quadratic Spectral kernel). Applicable to “Summary”, LLC, Fisher-PP, and Hadri tests. band = arg, b=arg (default=“nw”) Method of selecting the bandwidth: “nw” (Newey-West automatic variable bandwidth selection), “a” (Andrews automatic selection), number (user-specified common bandwidth), vector_name (user-specified individual band- width). Applicable to “Summary”, LLC, Fisher-PP, and Hadri tests. int 12T 100 1 4 ( )
- 512. uroot—505 Other options Examples The command: uroot(adf,const,lag=3,save=mout) gdp performs an ADF test on the series GDP with the test equation including a constant term and three lagged first-difference terms. Intermediate results are stored in the matrix MOUT. uroot(dfgls,trend,infosel=sic) ip runs the DFGLS unit root test on the series IP with a constant and a trend. The number of lagged difference terms is selected automatically using the Schwarz criterion. lag=arg Method of selecting lag length (number of first difference terms) to be included in the regression: “a” (automatic information criterion based selection), integer (user-speci- fied common lag length), vector_name (user-specific indi- vidual lag length). If the “balance” option is used, default= where is the length of the shortest cross-section or series, otherwise default=“a”. Applicable to “Summary”, LLC, Breitung, IPS, and Fisher- ADF tests. infosel=arg (default=“sic”) Information criterion to use when computing automatic lag length selection: “aic” (Akaike), “sic” (Schwarz), “hqc” (Hannan-Quinn). Applicable to “Summary”, LLC, Breitung, IPS, and Fisher- ADF tests. maxlag=arg Maximum lag length to consider when performing auto- matic lag length selection, where arg is an integer (com- mon maximum lag length) or a vector_name (individual maximum lag length) default= where is the length of the cross-section or series. prompt Force the dialog to appear from within a program. p Print output from the test. 1 if Tmin 60 2 if 60 T min 100 4 if Tmin 100 Tmin int mini 12 Ti 3,( ) Ti 100 1 4 ( ) Ti
- 513. 506—Chapter 12. Command Reference uroot(kpss,const,hac=pr,b=2.3) unemp runs the KPSS test on the series UNEMP. The null hypothesis is that the series is stationary around a constant mean. The frequency zero spectrum is estimated using kernel methods (with a Parzen kernel), and a bandwidth of 2.3. uroot(np,hac=ardt,infosel=maic) sp500 runs the NP test on the series SP500. The frequency zero spectrum is estimated using the OLS AR spectral estimator with the lag length automatically selected using the modified AIC. Cross-references See “Unit Root Testing” on page 545 of the User’s Guide II for discussion of standard unit root tests performed on a single series, and “Panel Unit Root Testing” on page 573 of the User’s Guide II for discussion of unit roots tests performed on panel structured workfiles, groups of series, or pooled data. Specify and estimate a VAR or VEC. Syntax varest(options) lag_pairs endog_list [@ exog_list] varest(method=ec, trend, n, options) lag_pairs endog_list [@ exog_list] The first form of the command estimates a VAR. It is the interactive command equivalent of using ls to estimate a named VAR object (see Var::ls (p. 852) in the Object Reference for syntax and details). The second form of the command estimates a VEC. It is the interactive command equivalent of using ec to estimate a named VAR object (see Var::ec (p. 842) in the Object Reference for syntax and details). Examples Examples varest 1 3 m1 gdp estimates an unnamed unrestricted VAR with two endogenous variables (M1 and GDP), a constant and 3 lags (lags 1 through 3). varest Interactive Use Commands prompt Force the dialog to appear from within a program. p Print output from the test.
- 514. wfclose—507 varest(noconst) 1 3 ml gdp estimates the same VAR, but with no constant term included in the specification. varest(method=ec) 1 4 m1 gdp tb3 estimates a VEC with four lagged first differences, three endogenous variables and one cointegrating equation using the default trend option “c”. varest(method=ec,b,2) 1 2 4 4 tb1 tb3 tb6 @ d2 d3 d4 estimates a VEC with lagged first differences of order 1, 2, 4, three endogenous variables, three exogenous variables, and two cointegrating equations using trend option “b”. Cross-references See also Var::var (p. 864), Var::ls (p. 852) and Var::ec (p. 842) in the Object Refer- ence. Close the active or specified workfile. Syntax wfclose(options) [name] wfclose allows you to close the currently active workfile. You may optionally provide the name of a workfile if you do not wish to close the active workfile. If more than one workfile is found with the same name, the one most recently opened will be closed. Options Examples wfclose closes the workfile that was most recently active. wfclose basics closes the workfile named BASICS. Cross-references See also wfcreate (p. 510), wfopen (p. 515) and wfsave (p. 529). wfclose Object Container, Data, and File Commands noerr Do not error if a name is provided and no workfile with that name is found.
- 515. 508—Chapter 12. Command Reference Compare the contents of the current workfile or page with the contents of a different work- file, page or database. Syntax wfcompare(options) targetspec baselinespec [@keep keeplist @drop droplist] The command shows a list of any differences between the objects specified by targetspec and those specified by baselinespec. targetspec should be a specification of objects in the current workfile. It should take the form of [page]name_pattern, where the optional page may be used to specify a specific page of the current workfile. name_pattern is used to list the objects you wish to compare. The typi- cal form of targetspec is simply “*”, meaning to compare all objects in the current workfile. baselinespec should be a specification of the list of objects to compare against. It should take the form of [container::page]name_pattern, where the optional container::page may be used to specify the name of the workfile or database or page containing the objects to com- pare against. name_pattern is used to list the objects you wish to compare against. If base- linespec is blank, the version of the current workfile stored on disk is used as the baseline. The optional @keep and @drop lists allow you to narrow further the list of objects in tar- getspec by listing specific objects to compare (using @keep), or drop from the targetspec (using @drop). Options wfcompare Object Container, Data, and File Commands tol=arg Specifies the threshold below which differences between the target and baseline values should be ignored. The threshold is specified as a fraction of the baseline value. For example, if tol=1%, one or more observations in the target object must differ from their values in the baseline object by at least one percent for the objects to be reported as dif- ferent. The default tolerance is 1e-15. seterr Set an error if any differences exceeding the specified toler- ance were found. This option may be useful in batch pro- gramming to alert the user if a program causes unexpectedly large changes to data values.
- 516. wfcompare—509 Examples wfcompare Compares the current workfile with the previously saved version of the workfile on disk. All pages are compared. wfcompare *_0 *_1 compares all in the current page with names ending with “_0” with all objects whose names ends in “_1”. wfcompare page1* page2* compares all objects in page1 of the current workfile with those in page2. wfcompare page1* page2* @drop GDP UNEMP compares all objects except for the objects “GDP” and “UNEMP” in page1 of the current workfile with those in page2. wfcompare * myfile.wf1 compares the contents of the current page with the contents of the same page in the saved workfile 'myfile.wf1' wfcompare ** jun2012.wf1 compares the contents of all pages in the current workfile in memory with all pages of the saved workfile “Jun2012.WF1”. wfcompare page2x* test.edb::y* compares all objects in page2 of the current workfile whose name begins with the letter “x” to all objects in the database test.edb whose name begins with the letter “y”. list=key Selects which objects should be included in the output list. key may be “a” (list objects that exist in the target but not baseline), “d” (list objects that exist in the baseline, but not target), “r” (list objects that exist in both but have different objects types), “c” (list objects that exist in both but have different frequencies), “m” (list objects that exist in both but have values that differ by more than the tolerance), “u” (list objects that exist in both and are unchanged), “s” (list objects that exist in both but cannot be compared). out=name Create a table object name, containing the comparison table. nohead Used with “out=” option to suppress the header row at the top of the frozen table.
- 517. 510—Chapter 12. Command Reference Cross-references See “Comparing Workfiles,” on page 88 of User’s Guide I for discussion. Create a new workfile. The workfile becomes the active workfile. Syntax wfcreate(options) frequency[(subperiod_opts)] start_date end_date [num_cross_sec- tions] wfcreate(options) u num_observations The first form of the command may be used to create a new regular frequency workfile with the specified frequency, start, and end date. Subperiod options may also be specified for intraweek or intraday data. See table below for a complete description for each frequency. If you include the optional num_cross_sections, EViews will create a balanced panel page using integer identifiers for each of the cross-sections. Note that more complex panel struc- tures may be created using pagestruct (p. 448). You may use the @now keyword to specify the current date/time as either the start_date or end_date. The second form of the command is used to create an unstructured workfile with the speci- fied number of observations. Options Arguments The frequency argument should be specified using one of the following forms: wfcreate Object Container, Data, and File Commands wf=wf_name Optional name for the new workfile. page=page_name Optional name for the page in the new workfile. prompt Force the dialog to appear from within a program. Sec[opt], 5Sec[opt], 15Sec[opt], 30Sec[opt] Seconds in intervals of: 1, 5, 15, or 30 seconds, respec- tively. You may optionally specify days of the week and start and end times during the day using the opt parameter. See explanation of subperiod options below.
- 518. wfcreate—511 Subperiod options EViews allows for setting the days of the week and the time of day within intraday frequen- cies, which include seconds, minutes, and hours. For instance, you may specify hourly data between 8AM and 5PM on Monday through Wednesday. These subperiod options should follow the frequency keyword and be enclosed in parentheses. To specify days of the week, use integers to indicate the days, where Monday is given the number 1 and Sunday is given the number 7. For example, wfcreate(wf=storehours) 30MIN(1-6, 8:00-17:00) 1/3/2000 12/30/2000 Min[opt], 2Min[opt], 5Min[opt], 10Min[opt], 15Min[opt], 20Min[opt], 30Min[[opt] Minutes in intervals of: 1, 2, 5, 10, 15, 20, or 30 minutes, respectively. You may optionally specify days of the week and start and end times during the day using the opt parameter. See explanation of subperiod options below. H[opt], 2H[opt], 4H[opt], 6H[opt], 8H[opt], 12H[[opt] Hours in intervals of: 1, 2, 4, 6, 8, or 12 hours, respectively. You may optionally specify days of the week and start and end times during the day using the opt parameter. See explanation of subperiod options below. D(s, e) Daily with arbitrary days of the week. Specify the first and last days of the week with integers s and e, where Monday is given the number 1 and Sunday is given the number 7. (Note that the “D” option used to specify a 5-day frequency prior to EViews 7.) D5 or 5 Daily with five days per week, Monday through Friday. D7 or 7 Daily with seven days per week. W Weekly T Ten-day (daily in intervals of ten). F Fortnight BM Bimonthly M Monthly Q Quarterly S Semi-annual A or Y Annual 2Y, 3Y, 4Y, 5Y, 6Y, 7Y, 8Y, 9Y, 10Y, 20Y Multi-year in intervals of: 2, 3, 4, 5, 6, 7, 8, 9, 10, or 20 years, respectively.
- 519. 512—Chapter 12. Command Reference indicates a half-hour frequency that includes Monday through Saturday from 8AM to 5PM. To specify the start and end times, you may use either a 24 hour clock, including minutes and optionally seconds, or a 12 hour clock using AM and PM. For example, each of the fol- lowing represents 8PM: 8PM, 8:00PM, 8:00:00PM, 20:00, and 20:00:00. Thus, our previous example could have been written: wfcreate(wf=storehours) 30MIN(1-6, 8AM-5PM) 1/3/2000 12/30/2000 If you wish to include all days of the week but would like to specify a start and end time, set the date range to include all days and then specify the times. The day of the week parameter appears first and is required if you wish to supply the time of day parameters. For instance, wfcreate(wf=storehours) 30MIN(1-7, 10AM-3PM) 1/3/2000 12/30/2000 indicates a half-hour frequency from 10AM to 3PM on all days of the week. You may also include a time with the start and end date parameters to specify partial days at the beginning or end of the workfile. For example, wfcreate(wf=storehours) 30MIN(1-6, 8AM-5PM) 1/3/2000 10AM 12/30/2000 2PM creates the same workfile as above, but limits the first day, 1/3/2000, to 10AM - 5PM and the last day, 12/30/2000, to 8AM - 2PM. Alignment options Certain frequencies optionally allow you to specify the starting point of the frequency period. Weekly and biweekly frequencies allow you to set the day at which the week begins. Annual, semiannual and quarterly frequencies allow you to set the month at which the quarter or year begins. Setting the starting period is important if you wish to use frequency conversion to convert data from a different frequency. To specify the start period, simply add an extra term to the frequency symbol, surrounded in parenthesis, containing the day, or month, upon which you wish the frequency to start. For example: wfcreate w(monday) 2000 2010 creates a weekly workfile from 2000 to 2010, where each week starts on a Monday. wfcreate a(july) 2001 2007 creates an annual workfile where each year starts in July. Note that by default, if you do not specify a starting point, EViews will use the period of the specified start_date. To make this difference concrete, consider the commands: wfcreate w 2000 2010 and
- 520. wfcreate—513 wfcreate w(monday) 2000 2010 Since January 1st, 2000 was a Saturday, the first command will create a weekly workfile where each week starts on a Saturday, and the first observation in the workfile will span the period January 1st-January 7th 2000. The second command will force EViews to start weeks on a Monday, and thus the first observation will actually span the period December 27th 1999 - January 2nd 2000. Examples wfcreate(wf=annual, page=myproject) a 1950 2005 wfcreate(wf=unstruct, page=undated) u 1000 creates two workfiles. The first is a workfile named ANNUAL containing a single page named MYPROJECT containing annual data from 1950 to 2005; the second is a workfile named UNSTRUCT containing a single page named UNDATED with 1000 unstructured observations. wfcreate(wf=griliches_grunfeld, page=annual) a 1935 1954 10 creates the GRILICHES_GRUNFELD workfile containing a page named “ANNUAL” with 10 cross-sections of annual data for the years 1935 to 1954. wfcreate(wf=fourday) D(1,4) 1/3/2000 12/31/2000 specifies a daily workfile from January 3, 2000 to December 31, 2000, including only Mon- day through Thursday. The day range may be delimited by either a comma or a dash, such that wfcreate(wf=fourday) D(1-4) 1/3/2000 12/31/2000 is equivalent to the previous command. wfcreate(wf=captimes) 15SEC(2-4) 1/3/2000 12/30/2000 creates a workfile with 15 second intervals on Tuesday through Thursday only, from 1/3/2000 to 12/30/2000. Cross-references See “Creating a Workfile,” on page 42 of User’s Guide I for discussion. See also pagecreate (p. 434) and pagedelete (p. 440).
- 521. 514—Chapter 12. Command Reference Change the details displayed in the current workfile window, optionally freezing the results into a table object in the current workfile. Syntax wfdetails(options) col1[(width1)] [col2[(width2)].....] @sort sortcol Specify the names of the attribute columns you would like to display in the workfile details view, optionally including the width of the column in parenthesis. Widths can be specified with positive numbers, indicating a width in pixels, or negative numbers if you wish to spec- ify the width in approximate characters. You may use the @sort keyword at the end of the specification to indicate by which column to sort the details view. Options Examples wfdetails start end shows the workfile details view, with the attribute columns “start” and “end”. wfdetails(out=dettable) start(30) end(-10) shows the same view, but setting the “start” column’s width to 30 pixels, and the “end” col- umn’s width to 10 characters, and freezing the view into a table called DETTABLE. wfdetails start end @sortcol type sorts the details view by object type. Cross-references See “Workfile Details Display,” on page 62 of User’s Guide I for discussion. wfdetails Object Container, Data, and File Commands out=name Create a table object name, containing the details view table. nohead Used with “out=” option to suppress the header row at the top of the frozen table.
- 522. wfopen—515 Change the workfile view to a simple object directory listing. Syntax wfdir If the workfile is currently in details view, the wfdir command switches the view back to a simple directory listing. Cross-references See “Workfile Details Display,” on page 62 of User’s Guide I for discussion. Open a workfile. Reads in a previously saved workfile from disk, or reads the contents of a foreign data source into a new workfile. The opened workfile becomes the default workfile; existing workfiles in memory remain on the desktop but become inactive. Syntax wfopen [path]source_name wfopen(options) source_description [table_description] [variables_description] wfopen(options) source_description [table_description] [dataset_modifiers] where path is an optional local path or URL. There are three basic forms of the wfopen command: • the first form is used by EViews native (“EViews and MicroTSP” on page 518) and time series database formats (“Time Series Formats” on page 518). • the second form used for raw data files—Excel, Lotus, ASCII text, and binary files (“Raw Data Formats” on page 519). • the third form is used with the remaining source formats, which we term dataset for- mats, since the data have already been arranged in named variables (“Datasets” on page 526). (See “Options” on page 517 for a description of the supported source formats and corre- sponding types.) wfdir Object Container, Data, and File Commands wfopen Object Container, Data, and File Commands
- 523. 516—Chapter 12. Command Reference In all three cases, the workfile or external data source should be specified as the first argu- ment following the command keyword and options. • In most cases, the external data source is a file, so the source_description will be the description of the file (including local path or URL information, if necessary). Alterna- tively, the external data source may be the output from a web server, in which case the URL should be provided. Similarly, when reading from an ODBC query, the ODBC DSN (data source name) should be used as the source_description. If the source_description contains spaces, it must be enclosed in (double) quotes. For raw and dataset formats, you may use table_description to provide additional informa- tion about the data to be read: • Where there is more than one table that could be formed from the specified external data source, a table_description may be provided to select the desired table. For exam- ple, when reading from an Excel file, an optional cell range may be provided to specify which data are to be read from the spreadsheet. When reading from an ODBC data source, a SQL query or table name must be used to specify the table of data to be read. • In raw data formats, the table_description allows you to provide additional informa- tion regarding names and descriptions for variables to be read, missing values codes, settings for automatic format, and data range subsetting. • When working with text or binary files, the table_description must be used to describe how to break up the file into columns and rows. For raw and dataset formats, you may use the dataset_modifiers specification to select the set of variables, maps (value labels), and observations to be read from the source data. The dataset_modifiers consists of the following keyword delimited lists: [@keep keep_list] [@drop drop_list] [@keepmap keepmap_list] [@dropmap drop- map_list] [@selectif condition] • The @keep and @drop keywords, followed by a list of names and patterns, are used to specify variables to be retain or dropped. Similarly, the @keepmap and @dropmap keywords followed by lists of name patterns controls the reading of value labels. The keyword @selectif, followed by an if condition (e.g., “if age>30 and gender=1”) may be used to select a subset of the observations in the original data. By default, all variables, value labels, and observations are read. By default, all variables, maps and observations in the source file will be read.
- 524. wfopen—517 Options For the most part, you should not need to specify a “type=” option as EViews will automat- ically determine the type from the filename. The following table summaries the various source formats and types, along with the corre- sponding “type=” keywords: type=arg / t=arg Optional type specification: (see table below). Note that ODBC support is provided only in the EViews Enterprise Edition. link Link the object to the source data so that the values can be refreshed at a later time. wf=wf_name Optional name for the new workfile. page=page_name Optional name for the page in the new workfile. prompt Force the dialog to appear from within a program. Source Type Option Keywords Access dataset “access” Aremos-TSD time series database “a”, “aremos”, “tsd” Binary raw data “binary” dBASE dataset “dbase” Excel (through 2003) raw data “excel” Excel 2007 (xml) raw data “excelxml” EViews Workfile native --- Gauss Dataset dataset “gauss” GiveWin/PcGive time series database “g”, “give” HTML raw data “html” Lotus 1-2-3 raw data “lotus” ODBC Dsn File dataset “dsn” ODBC Query File dataset “msquery” ODBC Data Source dataset “odbc” MicroTSP Workfile native “dos”, “microtsp” MicroTSP Mac Workfile native “mac” RATS 4.x time series database “r”, “rats” RATS Portable / TROLL time series database “l”, “trl” SAS Program dataset “sasprog”
- 525. 518—Chapter 12. Command Reference EViews and MicroTSP The syntax for EViews and MicroTSP files is: wfopen [path]workfile_name where path is an option local path or URL. Examples wfopen c:datamacro loads a previously saved EViews workfile “Macro.WF1” from the “data” directory in the C drive. wfopen c:tspnipa.wf loads a MicroTSP workfile “Nipa.WF”. If you do not use the workfile type option, you should add the extension “.WF” to the workfile name when loading a DOS MicroTSP work- file. An alternative method specifies the type explicitly: wfopen(type=dos) nipa The command: wfopen “<mydropboxdrive>”foldernipa.wf1” will open the file from the cloud location MYDROPBOXDRIVE. Time Series Formats The syntax for time series format files (Aremos-TSD, GiveWin/PcGive, RATS, RATS Porta- ble/TROLL, TSP Portable) is: wfopen(options) [path]source_name where path is an optional local path or URL. If the source files contain data of multiple frequencies, the resulting workfile will be of the lowest frequency, and higher frequency data will be converted to this frequency. If you wish to obtain greater control over the workfile creation, import, or frequency conversion pro- cesses, we recommend that you open the file using dbopen (p. 344) and use the database tools to create your workfile. SAS Transport dataset “sasxport” SPSS dataset “spss” SPSS Portable dataset “spssport” Stata dataset “stata” Text / ASCII raw data “text” TSP Portable time series database “t”, “tsp”
- 526. wfopen—519 Aremos Example wfopen dlcs.tsd wfopen(type=aremos) dlcs.tsd open the AREMOS-TSD file DLCS. GiveWin/PcGive Example wfopen "f:projectpc givedatamacrodata.in7" wfopen(type=give) "f:projectpc givedatamacrodata" open the PcGive file MACRODATA. Rats Examples wfopen macrodata.rat" wfopen macrodata.trl read the native RATS 4.x file MACRODATA.RAT and the RATS Portable/TROLL file “Macrodata.TRL”. TSP Portable Example wfopen macrodata.tsp reads the TSP portable file “Macrodata.TSP”. Raw Data Formats The command for reading raw data (Excel 97-2003, Excel 2007, HTML, ASCII text, Binary, Lotus 1-2-3) is wfopen(options) source_description [table_description] [variables_description] [@keep keep_list] [@drop drop_list] [@keepmap keepmap_list] [@dropmap dropmap_list] [@selectif condition] where the syntax of the table_description and variables_description differs slightly depend- ing on the type of file. Excel and Lotus Files The syntax for reading Excel and Lotus files is: wfopen(options) source_description [table_description] [variables_description] The following table_description elements may be used when reading Excel and Lotus data: • “range = arg”, where arg is a range of cells to read from the Excel workbook, follow- ing the standard Excel format [worksheet!][topleft_cell[:bottomright_cell]]. If the worksheet name contains spaces, it should be placed in single quotes. If the worksheet name is omitted, the cell range is assumed to refer to the currently active sheet. If only a top left cell is provided, a bottom right cell will be chosen automati-
- 527. 520—Chapter 12. Command Reference cally to cover the range of non-empty cells adjacent to the specified top left cell. If only a sheet name is provided, the first set of non-empty cells in the top left corner of the chosen worksheet will be selected automatically. As an alternative to specifying an explicit range, a name which has been defined inside the excel workbook to refer to a range or cell may be used to specify the cells to read. • “byrow”, transpose the incoming data. This option allows you to read files where the series are contained in rows (one row per series) rather than columns. The optional variables_description may be formed using the elements: • “colhead=int”, number of table rows to be treated as column headers. • “namepos = [first|last|all|none]”, which row(s) of the column headers should be used to form the column name. The setting “first” refers to first line, “last” is last line, “all” is all lines and “none” is no lines. The remaining column header rows will be used to form the column description. The default setting is “all”. • “Nonames”, the file does not contain a column header (same as “colhead=0”). • “names=("arg1","arg2",…)”, user specified column names, where arg1, arg2, … are names of the first series, the second series, etc. when names are provided, these over- ride any names that would otherwise be formed from the column headers. • “descriptions=("arg1","arg2",…)”, user specified descriptions of the series. If descriptions are provided, these override any descriptions that would otherwise be read from the data. • “types=("arg1","arg2",…)”, user specified data types of the series. If types are pro- vided they will override the types automatically detected by EViews. You may use any of the following format keywords: “a” (character data), “f” (numeric data), “d” (dates), or “w”(EViews automatic detection). • “na="arg1"”, text used to represent observations that are missing from the file. The text should be enclosed on double quotes. • “scan=[int| all]”, number of rows of the table to scan during automatic format detec- tion (“scan=all” scans the entire file). • “firstobs=int”, first observation to be imported from the table of data (default is 1). This option may be used to start reading rows from partway through the table. • “lastobs = int”, last observation to be read from the table of data (default is last observation of the file). This option may be used to read only part of the file, which may be useful for testing. Excel Examples wfopen "c:data filesdata.xls"
- 528. wfopen—521 loads the active sheet of DATA.XLS into a new workfile. wfopen(page=mypage) "c:data filesdata.xls" range="GDP data" @drop X reads the data contained in the “GDP data” sheet of “Data.XLS” into the MYPAGE page of a new workfile. The data for the series X is dropped, and the name of the new workfile page is “GDP”. To load the Excel file containing US Macro Quarterly data from Stock and Watson’s Introduc- tion to Econometrics you may use the command: wfopen http//wps.aw.com/wps/media/objects/3254/3332253/datasets2e/dat asets/USMacro_Quarterly.xls which will load the Excel file directly into EViews from the publisher’s website (as of 08/2009). HTML Files The syntax for reading HTML pages is: wfopen(options) source_description [table_description] [variables_description] The following table_description elements may be used when reading an HTML file or page: • “table = arg”, where arg specifies which table to read in an HTML file/page contain- ing multiple tables. When specifying arg, you should remember that tables are named automatically fol- lowing the pattern “Table01”, “Table02”, “Table03”, etc. If no table name is specified, the largest table found in the file will be chosen by default. Note that the table num- bering may include trivial tables that are part of the HTML content of the file, but would not normally be considered as data tables by a person viewing the page. • “skip = int”, where int is the number of rows to discard from the top of the HTML table. • “byrow”, transpose the incoming data. This option allows you to import files where the series are contained in rows (one row per series) rather than columns. The optional variables_description may be formed using the elements: • “colhead=int”, number of table rows to be treated as column headers. • “namepos = [first|last|all|none]”, which row(s) of the column headers should be used to form the column name. The setting “first” refers to first line, “last” is last line, “all” is all lines and “none” is no lines. The remaining column header rows will be used to form the column description. The default setting is “all”. • “nonames”, the file does not contain a column header (same as “colhead=0”).
- 529. 522—Chapter 12. Command Reference • “names=("arg1","arg2",…)”, user specified column names, where arg1, arg2, … are names of the first series, the second series, etc. when names are provided, these over- ride any names that would otherwise be formed from the column headers. • “descriptions=("arg1","arg2",…)”, user specified descriptions of the series. If descriptions are provided, these override any descriptions that would otherwise be read from the data. • “types=("arg1","arg2",…)”, user specified data types of the series. If types are pro- vided they will override the types automatically detected by EViews. You may use any of the following format keywords: “a” (character data), “f” (numeric data), “d” (dates), or “w”(EViews automatic detection). • “na="arg1"”, text used to represent observations that are missing from the file. The text should be enclosed on double quotes. • “scan=[int|all]”, number of rows of the table to scan during automatic format detec- tion (“scan=all” scans the entire file). • “firstobs=int”, first observation to be imported from the table of data (default is 1). This option may be used to start reading rows from partway through the table. • “lastobs = int”, last observation to be read from the table of data (default is last observation of the file). This option may be used to read only part of the file, which may be useful for testing. HTML Examples wfopen "c:data.html" loads into a new workfile the data located on the HTML file “Data.HTML” located on the C: drive wfopen(type=html) "http://www.tradingroom.com.au/apps/mkt/forex.ac" colhead=3, namepos=first loads into a new workfile the data with the given URL located on the website site “http://www.tradingroom.com.au”. The column header is set to three rows, with the first row used as names for columns, and the remaining two lines used to form the descriptions. Text and Binary Files The syntax for reading text or binary files is: wfopen(options) source_description [table_description] [variables_description] If a table_description is not provided, EViews will attempt to read the file as a free-format text file. The following table_description elements may be used when reading a text or binary file:
- 530. wfopen—523 • “ftype = [ascii|binary]” specifies whether numbers and dates in the file are stored in a human readable text (ASCII), or machine readable (Binary) form. • “rectype = [crlf|fixed|streamed]” describes the record structure of the file: “crlf”, each row in the output table is formed using a fixed number of lines from the file (where lines are separated by carriage return/line feed sequences). This is the default setting. “fixed”, each row in the output table is formed using a fixed number of charac- ters from the file (specified in “reclen= arg”). This setting is typically used for files that contain no line breaks. “streamed”, each row in the output table is formed by reading a fixed number of fields, skipping across lines if necessary. This option is typically used for files that contain line breaks, but where the line breaks are not relevant to how rows from the data should be formed. • “reclines =int”, number of lines to use in forming each row when “rectype=crlf” (default is 1). • “reclen=int”, number of bytes to use in forming each row when “rectype=fixed”. • “recfields=int”, number of fields to use in forming each row when “rec- type=streamed”. • “skip=int”, number of lines (if rectype is “crlf”) or bytes (if rectype is not “crlf”) to discard from the top of the file. • “comment=string“, where string is a double-quoted string, specifies one or more characters to treat as a comment indicator. When a comment indicator is found, everything on the line to the right of where the comment indicator starts is ignored. • “emptylines=[keep|drop]”, specifies whether empty lines should be ignored (“drop”), or treated as valid lines (“keep”) containing missing values. The default is to ignore empty lines. • “tabwidth=int”, specifies the number of characters between tab stops when tabs are being replaced by spaces (default=8). Note that tabs are automatically replaced by spaces whenever they are not being treated as a field delimiter. • “fieldtype=[delim|fixed|streamed|undivided]”, specifies the structure of fields within a record: “Delim”, fields are separated by one or more delimiter characters “Fixed”, each field is a fixed number of characters “Streamed”, fields are read from left to right, with each field starting immediately after the previous field ends. “Undivided”, read entire record as a single series.
- 531. 524—Chapter 12. Command Reference • “quotes=[single|double|both|none]”, specifies the character used for quoting fields, where “single” is the apostrophe, “double” is the double quote character, and “both” means that either single or double quotes are allowed (default is “both”). Characters contained within quotes are never treated as delimiters. • “singlequote“, same as “quotes = single”. • “delim=[comma|tab|space|dblspace|white|dblwhite]”, specifies the character(s) to treat as a delimiter. “White” means that either a tab or a space is a valid delimiter. You may also use the abbreviation “d=” in place of “delim=”. • “custom="arg1"”, specifies custom delimiter characters in the double quoted string. Use the character “t” for tab, “s” for space and “a” for any character. • “mult=[on|off]”, to treat multiple delimiters as one. Default value is “on” if “delim” is “space”, “dblspace”, “white”, or “dblwhite”, and “off” otherwise. • “endian = [big|little]”, selects the endianness of numeric fields contained in binary files. • “string = [nullterm|nullpad|spacepad]”, specifies how strings are stored in binary files. If “nullterm”, strings shorter than the field width are terminated with a single zero character. If “nullpad”, strings shorter than the field width are followed by extra zero characters up to the field width. If “spacepad”, strings shorter than the field width are followed by extra space characters up to the field width. • “byrow”, transpose the incoming data. This option allows you to import files where the series are contained in rows (one row per series) rather than columns. A central component of the table_description element is the format statement. You may specify the data format using the following table descriptors: • Fortran Format: fformat=([n1]Type[Width][.Precision], [n2]Type[Width][.Precision], ...) where Type specifies the underlying data type, and may be one of the following, I - integer F - fixed precision E - scientific A - alphanumeric X - skip and n1, n2, ... are the number of times to read using the descriptor (default=1). More complicated Fortran compatible variations on this format are possible. • Column Range Format: rformat="[n1]Type[Width][.Precision], [n2]Type[Width][.Precision], ...)"
- 532. wfopen—525 where optional type is “$” for string or “#” for number, and n1, n2, n3, n4, etc. are the range of columns containing the data. • C printf/scanf Format: cformat="fmt" where fmt follows standard C language (printf/scanf) format rules. The optional variables_description may be formed using the elements: • “colhead=int”, number of table rows to be treated as column headers. • “namepos = [first|last|all|none]”, which row(s) of the column headers should be used to form the column name. The setting “first” refers to first line, “last” is last line, “all” is all lines and “none” is no lines. The remaining column header rows will be used to form the column description. The default setting is “all”. • “nonames”, the file does not contain a column header (same as “colhead=0”). • “names=("arg1", "arg2",…)”, user specified column names, where arg1, arg2, … are names of the first series, the second series, etc. when names are provided, these over- ride any names that would otherwise be formed from the column headers. • “descriptions=("arg1", "arg2",…)”, user specified descriptions of the series. If descriptions are provided, these override any descriptions that would otherwise be read from the data. • “types=("arg1","arg2",…)”, user specified data types of the series. If types are pro- vided they will override the types automatically detected by EViews. You may use any of the following format keywords: “a” (character data), “f” (numeric data), “d” (dates), or “w”(EViews automatic detection). • “na="arg1"”, text used to represent observations that are missing from the file. The text should be enclosed on double quotes. • “scan=[int|all]”, number of rows of the table to scan during automatic format detec- tion (“scan=all” scans the entire file). • “firstobs=int”, first observation to be imported from the table of data (default is 1). This option may be used to start reading rows from partway through the table. • “lastobs = int”, last observation to be read from the table of data (default is last observation of the file). This option may be used to read only part of the file, which may be useful for testing. Text and Binary File Examples (.txt, .csv, etc.) wfopen c:data.csv skip=5, names=(gdp, inv, cons) reads “Data.CSV” into a new workfile page, skipping the first 5 rows and naming the series GDP, INV, and CONS.
- 533. 526—Chapter 12. Command Reference wfopen(type=text) c:date.txt delim=comma loads the comma delimited data DATE.TXT into a new workfile. wfopen(type=raw) c:data.txt skip=8, rectype=fixed, format=(F10,X23,A4) loads a text file with fixed length data into a new workfile, skipping the first 8 rows. The reading is done as follows: read the first 10 characters as a fixed precision number, after that, skip the next 23 characters (X23), and then read the next 4 characters as strings (A4). wfopen(type=raw) c:data.txt rectype=fixed, format=2(4F8,2I2) loads the text file as a workfile using the specified explicit format. The data will be a repeat of four fixed precision numbers of length 8 and two integers of length 2. This is the same description as “format=(F8,F8,F8,F8,I2,I2,F8,F8,F8,F8,I2,I2)”. wfopen(type=raw) c:data.txt rectype=fixed, rformat="GDP 1-2 INV 3 CONS 6-9" loads the text file as a workfile using column range syntax. The reading is done as follows: the first series is located at the first and second character of each row, the second series occupies the 3rd character, the third series is located at character 6 through 9. The series will named GDP, INV, and CONS. Datasets The syntax for reading data from the remaining sources (Access, Gauss, ODBC, SAS pro- gram, SAS transport, SPSS, SPSS portable, Stata) is: wfopen(options) source_description table_description [@keep keep_list] [@drop drop_list] [@selectif condition] Note that for this purpose we view Access and ODBC as datasets. ODBC or Microsoft Access The syntax for reading from an ODBC or Microsoft Access data source is wfopen(options) source_description table_description [@keep keep_list] [@drop drop_list] [@selectif condition] When reading from an ODBC or Microsoft Access data source, you must provide a table_de- scription to indicate the table of data to be read. You may provide this information in one of two ways: by entering the name of a table in the data source, or by including an SQL query statement enclosed in double quotes. Note that ODBC support is provided only in the EViews Enterprise Edition. ODBC Examples wfopen c:data.dsn CustomerTable
- 534. wfopen—527 opens in a new workfile the table named CUSTOMERTABLE from the ODBC database described in the DATA.DSN file. wfopen(type=odbc) "my server" "select * from customers where id>30" @keep p* opens in a new workfile with SQL query from database using the server “MY SERVER”, keeping only variables that begin with P. The query selects all variables from the table CUS- TOMERS where the ID variable takes a value greater than 30. Other Dataset Types The syntax for reading data from the remaining sources (Gauss, SAS program, SAS trans- port, SPSS, SPSS portable, Stata) is: wfopen(options) source_description [@keep keep_list] [@drop drop_list] [@selectif condition] Note that no table_description is required. SAS Program Example If a data file, “Sales.DAT”, contains the following space delimited data: AZ 110 1002 CA 200 2003 NM 90 908 OR 120 708 WA 113 1123 UT 98 987 then the following SAS program file can be read by EViews to open the data: Data sales; infile sales.dat'; input state $ price sales; run; SAS Transport Examples wfopen(type=sasxport) c:data.xpt loads a SAS transport file “data.XPT” into a new workfile. wfopen c:inst.sas creates a workfile by reading from external data using the SAS program statements in “Inst.SAS”. The program may contain a limited set of SAS statements which are commonly used in reading in a data file.
- 535. 528—Chapter 12. Command Reference Stata Examples To load a Stata file “Data.DTA” into a new workfile, dropping maps MAP1 and MAP2, you may enter: wfopen c:data.dta @dropmap map1 map2 To download the sports cards dataset from Stock and Watson’s Introduction to Econometrics you may use the command: wfopen http://wps.aw.com/wps/media/objects/3254/3332253/datasets2e/da tasets/Sportscards.dta which will load the Stata dataset directly into EViews from the publisher’s website (as of 08/2009). Cross-references See Chapter 3. “Workfile Basics,” on page 41 of User’s Guide I for a discussion of workfiles. See also pageload (p. 440), read (p. 458), fetch (p. 355), wfsave (p. 529), wfclose (p. 507) and pagesave (p. 442). Refresh all links and auto-series in the active workfile. Primarily used to refresh links that use external database data. Syntax wfrefresh Examples wfrefresh Cross-references See “Creating a Database Link” on page 366 and “Understanding the Cache” on page 367 of the User’s Guide I. See also Chapter 8. “Series Links,” on page 223 of the User’s Guide I for a description of link objects, and “Auto-Updating Series” on page 193 of the User’s Guide I for a discussion of auto-updating series. See also pagerefresh (p. 441), Link::link (p. 352) and Link::linkto (p. 353) in the Object Reference. wfrefresh Object Container, Data, and File Commands
- 536. wfsave—529 Save the default workfile as an EViews workfile (.wf1 file) or as a foreign file or data source. Syntax wfsave(options) [path]filename wfsave(options) source_description [@keep keep_list] [@drop drop_list] [@keepmap keepmap_list] [@dropmap dropmap_list] [@smpl smpl_spec] wfsave(options) source_description table_description [@keep keep_list] [@drop drop_list] [@keepmap keepmap_list] [@dropmap dropmap_list] [@smpl smpl_spec] saves the active workfile in the specified directory using filename. By default, the workfile is saved as an EViews workfile, but options may be used to save all or part of the active page in a foreign file or data source. See wfopen (p. 515) for details on the syntax for source_descrip- tions and table_descriptions. Note, in particular, that you may use the byrow table_descrip- tion for Excel 2007 files to instruct EViews to save the series by row (instead of the standard by column). Options Workfile Save Options The default workfile save settings use the global options. wfsave Object Container, Data, and File Commands 1 Save using single precision. 2 Save using double precision. c Save compressed workfile (not compatible with EViews versions prior to 5.0).
- 537. 530—Chapter 12. Command Reference Foreign Source Save Options The following table summaries the various formats along with the corresponding “type=” keywords: type=arg, t=arg Optional type specification: (see table below). Note that ODBC support is provided only in the EViews Enterprise Edition. mode=arg Specify whether to create a new file, overwrite an existing file, or update an existing file. arg may be “create” (create new file only; error on attempt to overwrite) or “update” (only available for Excel files, and only if Excel is installed) (update an existing file, only overwriting the area specified by the range= table_description). If the “mode=” option is not used, EViews will create a new file, unless the file already exists in which case it will overwrite it. maptype=arg Write selected maps as: numeric (“n”), character (“c”), both numeric and character (“b”). nomapval Do not write mapped values for series with attached value labels (the default is to write the mapped values if avail- able). noid Do not write observation identifiers to foreign data files (by default, EViews will include a column with the date or observation identifier). nonames Do not write variable names (only applicable to file for- mats that support writing raw data without variable names). na=arg String value to be used for NAs. Option Keywords Access “access” Aremos-TSD “a”, “aremos”, “tsd” Binary “binary” dBASE “dbase” Excel (through 2003) “excel” Excel 2007 (xml) “excelxml” EViews Workfile --- Gauss Dataset “gauss” GiveWin/PcGive “g”, “give” HTML “html”
- 538. wfsave—531 Note that if you wish to save your Excel 2007 XML file with macros enabled, you should specify an explicit filename extension of “.XLSM”. Examples wfsave new_wf saves the current EViews workfile as “New_wf.WF1” in the default directory. wfsave "c:documents and settingsmy dataconsump" saves the current workfile as “Consump.WF1” in the specified path. wfsave macro @keep gdp unemp saves the two series GDP and UNEMP in a separate workfile, “macro.WF1” in the default directory. wfsave macro @dropmap gdp* saves all of the series in the current workfile, other than those that match the name pattern “gdp*” in a workfile, “macro.WF1” in the default directory. wfsave(type=excel) macro @keep gdp unemp saves the two series GDP and UNEMP as an Excel 2003 file, “macro.xls” in the default direc- tory. wfsave(type=excelxml, mode=update) macro.xlsx range="Sheet2!a1" byrow @keep gdp unemp Lotus 1-2-3 “lotus” ODBC Dsn File “dsn” ODBC Data Source “odbc” MicroTSP Workfile “dos”, “microtsp” MicroTSP Mac Workfile “mac” RATS “r”, “rats” RATS Portable / TROLL “l”, “trl” SAS Program “sasprog” SAS Transport “sasxport” SPSS “spss” SPSS Portable “spssport” Stata “stata” Text / ASCII “text” TSP Portable “t”, “tsp”
- 539. 532—Chapter 12. Command Reference will save the two series GDP and UNEMP into the existing Excel 2007 file “macro.xlsx”, specifying that the series should be written by row, starting in cell A1 on sheet Sheet2. To save the latter file in a macro-enabled Excel 2007 file, you should specify the explicit file- name extension “.XLSM”: wfsave(type=excelxml, mode=update) macro.xlsm range="Sheet2!a1" byrow @keep gdp unemp The command: wfsave “<mydropboxdrive>”foldernipa.wf1” will save the file to the cloud location MYDROPBOXDRIVE. Cross-references See Chapter 3. “Workfile Basics,” on page 41 of the User’s Guide I for a discussion of work- files. See also pagesave (p. 442), wfopen (p. 515), and pageload (p. 440). Make the selected workfile page the active workfile page. Syntax wfselect wfname[pgname] where wfname is the name of a workfile that has been loaded into memory. You may option- ally provide the name of a page in the new default workfile that you wish to be made active. Examples wfselect myproject wfselect myprojectpage2 both change the default workfile to MYPROJECT. The first command uses the default active page, while the second changes the page to PAGE2. Cross-references See Chapter 3. “Workfile Basics,” on page 41 of the User’s Guide I for a discussion of work- files. See also pageselect (p. 444). wfselect Object Container, Data, and File Commands
- 540. wfunlink—533 Display the workfile statistics and summary view. Syntax wfstats [wfname] displays the workfile statistics and summary view showing, for each page of the specified workfile, the structure of the page, and a summary of the objects contained in the page. The specified workfile must be open. If no workfile is specified, wfstats will display results for the active workfile. Examples wfstats displays the statistics view for the active workfile. wfstats wf2 displays the statistics for the open workfile WF2. Cross-references See “Workfile Summary View” on page 75 of the User’s Guide I for a description of the workfile statistics and summary view. Break links in all link objects and auto-updating series (formulae) in the active workfile. You should use some caution with this command as you will not be prompted before the links and auto-updating series are converted. Syntax wfunlink Examples wfunlink breaks links in all pages of the active workfile wfstats Object Container, Data, and File Commands wfunlink Object Container, Data, and File Commands
- 541. 534—Chapter 12. Command Reference Cross-references See Chapter 8. “Series Links,” on page 223 of User’s Guide I for a description of link objects, and “Auto-Updating Series” on page 193 of User’s Guide I for a discussion of auto-updating series. See also Link::link (p. 352) and Link::linkto (p. 353) in the Object Reference. See also pageunlink (p. 450) and unlink (p. 501) for page and object based unlinking, respectively. Activates a workfile. If the workfile is currently open in EViews, then it is selected to become the default workfile. If the workfile is not currently open, it is opened. Syntax wfuse [path]workfile_name[.wf1][page_name] The name of the workfile is specified as the argument to wfopen and may be followed by an optional page name to specify a specific page in the workfile. If the workfile is not located in the current default directory, and is not currently open in EViews, you should specify the path of the workfile along with its name. If you do specify a path, you should also include the .WF1 extension. Examples wfuse basics activates the BASICS workfile. If BASICS is not currently open in EViews and is not located in the current default directory, wfuse will error. wfuse c:mydatabasics.wf1 activates the BASICS workfile located in “c:mydata”. wfuse c:mydatabasics.wf1page1 activates the page named PAGE1 in the BASICS workfile located in “C:MYDATA”. Cross-references See Chapter 3. “Workfile Basics,” on page 41 of the User’s Guide I for a discussion of work- files. See also wfopen (p. 515) and wfsave (p. 529). wfuse Object Container, Data, and File Commands
- 542. write—535 Create or change workfiles. No longer supported; provided for backward compatibility. This command has been replaced by wfcreate (p. 510) and pageselect (p. 444). Write EViews data to a text (ASCII), Excel, or Lotus file on disk. Creates a foreign format disk file containing EViews data. May be used to export EViews data to another program. Unless you need to write your workfile data in transposed form, we recommend that you use the more powerful command for writing a workfile page documented in pagesave (p. 442). Syntax write(options) [path]filename arg1 [arg2 arg3 …] Follow the keyword by a name for the output file and list the series to be written. The optional path name may be on the local machine, or may point to a network drive. If the path name contains spaces, enclose the entire expression in double quotation marks. Note that EViews cannot, at present, write into an existing file. The file that you select will, if it exists, be replaced. Options Options are specified in parentheses after the keyword and are used to specify the format of the output file. File type If you omit the “t=” option, EViews will determine the type based on the file extension. Unrecognized extensions will be treated as ASCII files. For Lotus and Excel spreadsheet files workfile Object Container, Data, and File Commands write Object Container, Data, and File Commands prompt Force the dialog to appear from within a program. t=dat, txt ASCII (plain text) files. t=wk1, wk3 Lotus spreadsheet files. t=xls Excel spreadsheet files.
- 543. 536—Chapter 12. Command Reference specified without the “t=” option, EViews will automatically append the appropriate exten- sion if it is not otherwise specified. ASCII text files Spreadsheet (Lotus, Excel) files Examples write(t=txt,na=.,d=c,dates) a:dat1.csv hat1 hat_se1 Writes the two series HAT1 and HAT_SE1 into an ASCII file named DAT1.CSV on the A drive. The data file is listed by observations, NAs are coded as “.” (dot), each series is sepa- rated by a comma, and the date/observation numbers are written together with the series names. write(t=txt,na=.,d=c,dates) dat1.csv hat1 hat_se1 writes the same file in the default directory. Cross-references See “Exporting to a Spreadsheet or Text File” on page 154 of User’s Guide I for a discussion. See pagesave (p. 442) for a superior method of exporting workfile pages to foreign formats. na=string Specify text string for NAs. Default is “NA”. names (default) / nonames [Write / Do not write] series names. dates / nodates [Write / Do not write] dates/obs. “Dates” is the default unless the “t” option for writing by series is used, in which case “nodates” is the default. d=arg Specify delimiter (default is tab): “s” (space), “c” (comma). t Write by series. Default is to write by obs with series in col- umns. letter_number Coordinate of the upper-left cell containing data. names (default) / nonames [Write / Do not write] series names. dates (default) / nodates [Write / Do not write] dates/obs. dates=arg Excel format for writing date: “first” (convert to the first day of the corresponding observation if necessary), “last” (convert to the last day of the corresponding observation). t Write by series. Default is to write by obs with series in col- umns.
- 544. xget—537 See also read (p. 458). Close an open connection to an external application. Syntax xclose xclose is used to close an open COM session with an external application. Examples xclose closes the open connection. Cross-references See “EViews COM Automation Client Support (MATLAB and R),” beginning on page 177 for discussion. See also xopen (p. 540), xget (p. 537), xput (p. 542), xrun (p. 544), and xlog (p. 540). Retrieve data from an external application into an EViews object. Syntax xget(options) object @smpl sample xget is used to retrieve data from an external COM application (support is currently pro- vided for MATLAB and R). An existing connection to the external application must have already been opened using xopen (p. 540). The xget command should be followed by the name of the object to retrieve, followed by an optional @smpl keyword and a sample speci- fication, or the name of a sample object. Including @smpl lets you specify the observations into which the data will be retrieved. Options xclose External Interface Commands xget External Interface Commands name=arg Specify the name of the object to be created in EViews. If the name option is not specified, the created object will have the same name as the external application object that is being retrieved.
- 545. 538—Chapter 12. Command Reference R-specific options wf=arg Specify the workfile into which the retrieved objects will be placed. The specified workfile must be currently open inside EViews. If the wf option is not specified, the objects will be put into the current default workfile. page=arg Specify the workfile page into which the retrieved objects will be placed. If the page option is not specified, the objects will be put into the current default page. type=arg Specify the EViews object type to be created. arg can be “series”, “alpha”, “matrix”, “vector”, “svector”, “coef”, “sym”, “scalar” or “string”. If the type option is not set, EViews will automatically decide which object type to cre- ate. mode = arg (default=“merge”) Merge options: “protect” (protect destination – only retrieve values if destination does not already exist), “merge” (prefer source -– merge only if source value is non-missing), “mergedest” (prefer destination – merge only if destination value is missing), “update” (replace all destination values in the retrieval sample with source val- ues), “overwrite” (replace all destination values in retrieval sample with source values, and NAs outside of sample). fdata=arg When reading a factor object, specifies how to read the fac- tor data: as numbers (default), as labels (“labels”), as both numbers and labels (“both”). If “fdata=both”, the labels will be read into a valmap object, and the valmap will be attached to the destination series (the data target must be a series for this setting). fmap=arg Name of the valmap object to hold the factor labels (when “fdata=both”).
- 546. xget—539 MATLAB-specific options Examples xget X retrieves an object “X” from the current open external application connection, and stores it into the current default workfile. xget(type=vector, name=x1) Ymat retrieves the object named “Ymat” and stores it into the current default workfile as a vector named X1. xget(wf=mywf, type=series, name=x2) X @smpl 1990 1999 retrieves X and stores it in the MYWF workfile as a series called X2, where only the observa- tions between 1990 and 1999 are filled in. Cross-references See “EViews COM Automation Client Support (MATLAB and R),” beginning on page 177 for discussion. See also xopen (p. 540), xclose (p. 537), xput (p. 542), xrun (p. 544) and xlog (p. 540). fmode=arg Specifies settings for reading factor label data into a val- map: merge with preference to the existing values in the map (default), merge with preference to the factor map values (“merge”), overwrite existing valmap definitions (“overwrite”), do not alter an existing valmap (“protect”). The default method adds definitions from the factor to an existing valmap, retaining existing valmap entries that con- flict with definitions from the factor. “Merge” adds definitions from the factor to an existing val- map, replacing conflicting valmap entries with definitions from the factor. “Overwrite” replaces an existing valmap specification with the factor definitions. “Protect” ensures that an existing valmap is not altered. workspace=arg (default=“base”) MATLAB environment in which to obtain the data: “base” (base workspace), “global” (global workspace).
- 547. 540—Chapter 12. Command Reference Switch on or off the external application log inside EViews. Syntax xlog(arg) xlog is used to switch on or off the external COM application log window inside EViews. arg should be either “show” (to switch the log on), or “hide” (to switch it off). Examples xlog(hide) switches off the log. Cross-references See “EViews COM Automation Client Support (MATLAB and R),” beginning on page 177 for discussion. See also xopen (p. 540), xclose (p. 537), xget (p. 537), xput (p. 542), and xrun (p. 544). Open a connection to an external application. Syntax xopen(options) xopen is used to start a COM session with an external application, either R or MATLAB. EViews can only have a single session open at a time; a session must be closed (see xopen (p. 540)) before a new session can be opened. Options xlog External Interface Commands xopen External Interface Commands type=arg Set the type of connection to be opened. arg may be “r” (R) or “m” (MATLAB). progid=arg (optional) Set the version of MATLAB or statconnDCOM to which EViews connects when opening a session. If not specified, EViews will use the default ProgID speci- fied in the global options.
- 548. xopen—541 Note that the MATLAB ProgIDs may be of particular interest as MATLAB (R2008a and later) offers several distinct ways in which to connect to the server. The relevant ProgIDs are: 1. “MATLAB.Application”— this ProgID starts a command window version of MAT- LAB that was most recently used as a server (might not be the latest installed ver- sion of MATLAB). 2. “MATLAB.Application.Single”— same as (1) but starts a dedicated server so that other programs looking to use MATLAB cannot connect to your instance. 3. “MATLAB.Autoserver”—starts a command window server using the most recent version of MATLAB. 4. “MATLAB.Autoserver.Single”—same as (3) but starts a dedicated server. 5. “MATLAB.Desktop.Application”—starts the full desktop MATLAB as a server using the most recent version of MATLAB. Each ProgID may be amended to indicate a specific version of MATLAB. For example, using the ProgID: MATLAB.Desktop.Application.7.6 instructs EViews to use the full desktop MATLAB GUI for version R2008a (v7.6) as the Auto- mation server. Examples xopen(type=m) opens a connection to MATLAB. xopen(type=r, case=lower) opens a connection to R and sets the default name-case to lower. xopen(type=m, progid=MATLAB.Desktop.Application.7.9) opens a connection to MATLAB 7.9 running with the full desktop GUI. nolog Do not open a session log window. case=arg Specify the default case for objects in R or MATLAB using xput (p. 542). If “case=” is not provided, the default case specified in the global options will be assumed. Note that once a connection has been opened, the case option cannot be changed; you may however, use the “name=” option when using xput (p. 542) to provide an explicit name.
- 549. 542—Chapter 12. Command Reference Cross-references See “EViews COM Automation Client Support (MATLAB and R),” beginning on page 177 for discussion. See also “External Program Interface” on page 828 of User’s Guide I for global options setting. See xclose (p. 537), xget (p. 537), xput (p. 542), xrun (p. 544), and xlog (p. 540). Send an EViews object to an external application. Syntax xput(options) ob_list @drop ob1 @smpl sample xput is used to push data from EViews to an COM external application (either R or MAT- LAB). An existing connection to the external application must have already been opened, using xopen. ob_list should be a space delimited list of EViews objects to push into the application. An asterisk (*) can be used as a wildcard. Objects that can be pushed include series, alphas, matrices, syms, vectors, svectors, rowvectors, coefs, scalars and strings. if the object list con- tains an object you do not wish to be pushed to the external application, you can use @drop followed by the name of the object. For series and alphas, you may enter a sample to specify which observations you wish to be pushed, using @smpl followed by the sample specification or the name of a sample object. Options xput External Interface Commands name=arg Specify the name or names of the objects to be created in the destination application. Multiple names may be speci- fied using a wildcard or a space-delimited list of names. Names specified using the name option are case-sensitive so that destination objects will preserve the case of arg. If the name option is not specified, the created objects will have the same name as the EViews objects that are being pushed, with the case determined by the case established for the COM connection (see xopen (p. 540)). mode=arg (default= “overwrite”) Merge options: “protect” (protect destination – only put values if destination does not already exist), “overwrite” (replace all destination values with source values, and resize if necessary).
- 550. xput—543 R-specific options MATLAB-specific options Examples xopen(type=m) xput(name=g) group01 opens a connection to MATLAB and then pushes the group GROUP01 to MATLAB, giving it the name G. xopen(type=r) xput(page=page2, rtype=vector) x @smpl 1990 1999 Opens a connection to R and pushes the series X from page PAGE2 of the current default workfile into a vector in R. Only the 10 observations for X from 1990 and 1999 are pushed into R. X will be named in R using the name “X” with the default case specified in the global options. xopen(type=r, case=upper) xput(rtype=data.frame, name=df1) x* @drop x2 Opens a connection to R and puts all objects whose name starts with “X”, apart from the object X2, into a data frame, named “df1”. The names of the “X*” objects will be uppercased in R. wf=arg Specify the workfile containing the objects to be pushed. The specified workfile must be currently open inside EViews. If the “wf=” option is not specified, the objects will be taken from the current default workfile. page=arg Specify the workfile page containing the objects to be pushed. If the “page=” option is not specified, the objects will be taken from the current default page. map Write value-mapped series using map labels instead of underlying values. rtype=arg Specify the type of object to be created in R. arg can be “vector”, “ts”, “data.frame” or “list”. mtype =arg Specify the type of object to be created in MATLAB: “matrix” (matrix object), “cell” (cell object). workspace=arg (default=“base”) MATLAB environment in which to place the data: “base” (base workspace), “global” (global workspace).
- 551. 544—Chapter 12. Command Reference Cross-references See “EViews COM Automation Client Support (MATLAB and R),” beginning on page 177 for discussion. See also “External Program Interface” on page 828 of User’s Guide I for global options setting of the default case for names. See also @xputnames (p. 650), xopen (p. 540), xclose (p. 537), xget (p. 537), xrun (p. 544), and xlog (p. 540). Run a command in an external application. Syntax xrun command xrun is used to run a command in an external COM application (either R or MATLAB). An existing connection to the external application must have already been opened using xopen. xrun should be followed by the command you wish to run, surrounded in quotes. Examples xopen(type=m, case=upper) xput Y xput XS xrun "beta = inv(XS'*XS)*XS'*Y" opens a connection to MATLAB, sends the series Y and the group XS to MATLAB, then runs a command which will compute the least squares coefficients from a regression of Y on XS. The commands xopen(type=r, case=upper) xput(rtype=data.frame, name=cancer) age drug2 drug3 studytim xrun z<-glm(STUDYTIM~AGE+1+DRUG2+DRUG3, family=Gamma(link=log),data=cancer) send data to R and estimate GLM model. Note that the statconnDCOM package does not always automatically capture all of your R output. Consequently, you may find that using xrun to issue a command that displays out- put in R may only return a subset of the usual output to your log window. In the most extreme case, you may see a simple “OK” message displayed in your log window. To instruct statconnDCOM to show all of the output, you should use enclose your command inside an explicit print statement in R. Thus, to display the contents of a matrix X, you must issue the command xrun External Interface Commands
- 552. xrun—545 xrun print(X) instead of the more natural xrun X Cross-references See “EViews COM Automation Client Support (MATLAB and R),” beginning on page 177 for a discussion. See also xopen (p. 540), xclose (p. 537), xget (p. 537), xput (p. 542), and xlog (p. 540).
- 553. 546—Chapter 12. Command Reference
- 554. Chapter 13. Operator and Function Reference The reference material in this section describes basic operators and functions that may be used with series and (in some cases) matrix objects. A general description of the use of these operators and functions may be found in Chapter 6. “Working with Data,” beginning on page 169 of User’s Guide I. This material is divided into several topics: • Operators. • Basic mathematical functions. • Time series functions. • Financial functions. • Descriptive statistics. • Cumulative statistics functions. • Moving statistics functions. • Group row functions. • By-group statistics. • Additional and special functions. • Trigonometric functions. • Statistical distribution functions. • String functions. • Date functions. • Indicator functions. • Workfile and informational functions. • Value map functions. Extensive documentation on libraries of more specialized functions is provided elsewhere: • For a description of functions related to string and date manipulation, see “String Function Summary” on page 611 and “Date Function Summary” on page 612. • For discussion of the workfile functions that provide information about each observa- tion of a workfile based on information contained in the structure of the workfile, see Chapter 15. “Workfile Functions,” on page 593.
- 555. 548—Chapter 13. Operator and Function Reference • Functions for working with value maps are documented in “Valmap Functions” on page 218 of the User’s Guide I. • For a list of functions specific to matrices, see “Matrix Command and Function Sum- mary” on page 653. Operators All of the operators described below may be used in expressions involving series and scalar values. When applied to a series expression, the operation is performed for each observation in the current sample. The precedence of evaluation is listed in “Operators” on page 169. Note that you can enforce order-of-evaluation using parentheses. In addition, EViews provides special functions to perform comparisons using special rules for handling missing values (see “Missing Values” on page 177): Expression Operator Description + add x+y adds the contents of X and Y. – subtract x–y subtracts the contents of Y from X. * multiply x*y multiplies the contents of X by Y. / divide x/y divides the contents of X by Y. ^ raise to the power x^y raises X to the power of Y. > greater than x>y takes the value 1 if X exceeds Y, and 0 other- wise. < less than x<y takes the value 1 if Y exceeds X, and 0 other- wise. = equal to x=y takes the value 1 if X and Y are equal, and 0 otherwise. <> not equal to x<>y takes the value 1 if X and Y are not equal, and 0 if they are equal. <= less than or equal to x<=y takes the value 1 if X does not exceed Y, and 0 otherwise. >= greater than or equal to x>=y takes the value 1 if Y does not exceed X, and 0 otherwise. and logical and x and y takes the value 1 if both X and Y are non- zero, and 0 otherwise. or logical or x or y takes the value 1 if either X or Y is nonzero, and 0 otherwise.
- 556. Basic Mathematical Functions—549 Basic Mathematical Functions The following functions perform basic mathematical operations. When applied to a series, they return a value for every observation in the current sample. When applied to a matrix object, they return a value for every element of the matrix object. The functions will return NA values for observations where the input values are NAs, or where the input values are not valid. For example, the square-root function @sqrt, will return NAs for all observations less than zero. Note: @iff, @inv, and @recode do not work with matrix objects. @eqna(x,y) equal to takes the value 1 if X and Y are equal, and 0 otherwise. NAs are treated as ordinary values for purposes of comparison. @isna(x) equal to NA takes the value 1 if X is equal to NA and 0 otherwise. @neqna(x,y) not equal to takes the value 1 if X and Y are not equal, and 0 if they are equal. NAs are treated as ordi- nary values for purposes of comparison. Name Function Examples/Description @abs(x), abs(x) absolute value @abs(-3)=3. @ceiling(x) smallest integer not less than @ceiling(2.34)=3, @ceiling(4)=4. @exp(x), exp(x) exponential, @exp(1)=2.71813. @fact(x) factorial, @fact(3)=6, @fact(0)=1. @factlog(x) natural logarithm of the facto- rial, @factlog(3)=1.79176, @factlog(0)=0. @floor(x) largest integer not greater than @floor(1.23)=1, @floor(-3.1)=-4. @iff(s,x,y) recode by condition returns if condition is true; other- wise returns . Note this is the same as @recode. @inv(x) reciprocal, inv(2)=0.5 (For series only; you should use @einv to obtain the element inverse of a matrix). @mod(x,y) floating point remainder returns the remainder of with the same sign as . If the result is 0. @log(x), log(x) natural logarithm, @log(2)=0.693..., log(@exp(1))=1. @log10(x) base-10 logarithm, @log10(100)=2. e x x! loge x! x s y 1 x x y x y 0 loge x log10 x
- 557. 550—Chapter 13. Operator and Function Reference Time Series Functions The following functions facilitate working with time series data. Note that NAs will be returned for observations for which lagged values are not available. For example, d(x) returns a missing value for the first observation in the workfile, since the lagged value is not available. @logx(x,b) base-b logarithm, @logx(256,2)=8. @nan(x,y) recode NAs in X to Y returns if , and if . @recode(s,x,y) recode by condition returns if condition is true; other- wise returns . @round(x) round to the nearest integer @round(-97.5)=-98, @round(3.5)=4. @sqrt(x), sqr(x) square root @sqrt(9)=3. Name Function Description d(x) first difference where is the lag operator. d(x,n) n-th order difference . d(x,n,s) n-th order difference with a sea- sonal difference at . dlog(x) first difference of the logarithm . dlog(x,n) n-th order difference of the loga- rithm . dlog(x,n,s) n-th order difference of the loga- rithm with a seasonal difference at . @lag(x, n) n-th order lag (equivalent to “X(-4”) for the series X) @pc(x) one-period percentage change (in percent) equals @pch(x)*100 @pch(x) one-period percentage change (in decimal) . @pca(x) one-period percentage change— annualized (in percent) equals @pcha(x)*100 logb x x x<>NA y x NA x s y 1 L– X X X 1– – L 1 L– n X s 1 L– n 1 L s – X 1 L– X log X log X 1– log– 1 L– n X log s 1 L– n 1 L s – X log X n– X X 1– – X 1–
- 558. Financial Functions—551 Financial Functions The following series-only functions permit you to perform calculations commonly employed in financial analysis. The functions are evaluated for each observation in the workfile sam- ple. @pcha(x) one-period percentage change— annualized (in decimal) where is the lag associated with one-year ( ) for quarterly data, etc.). @pcy(x) one-year percentage change (in percent) equals @pchy(x)*100 @pchy(x) one-year percentage change (in decimal) , where is the lag associated with one-year ( ) for annual data, etc.). Name Function Description @fv(r,n,x[,fv,t]) future value future value of an -period annu- ity with rate , payments , and optional final lump sum payment . A non-zero value for the optional indicates that the payments are made at the beginning of periods instead of ends. @nper(r,x,pv[,fv,t]) number of periods number of annuity periods required to produce the present value with rate , payments and optional final lump sum pay- ment . A non-zero value for the optional indicates that the payments are made at the beginning of periods. @pv(r,n,x[,fv,t]) present value present value of an -period annuity with rate , payments , and optional final lump sum pay- ment . A non-zero value for the optional indicates that the payments are made at the beginning of periods. @pcha(x) 1 @pch x n 1– n n 4 X X n– – X n– n n 12 n r x fv t pv r x fv t n r x fv t
- 559. 552—Chapter 13. Operator and Function Reference Descriptive Statistics These functions compute descriptive statistics for a specified sample, excluding missing val- ues if necessary. The default sample is the current workfile sample. If you are performing these computations on a series and placing the results into a series, you can specify a sam- ple as the last argument of the descriptive statistic function, either as a string (in double quotes) or using the name of a sample object. For example: series z = @mean(x, "1945m01 1979m12") or w = @var(y, s2) where S2 is the name of a sample object and W and X are series. Note that you may not use a sample argument if the results are assigned into a matrix, vector, or scalar object. For example, the following assignment: vector(2) a series x a(1) = @mean(x, "1945m01 1979m12") is not valid since the target A(1) is a vector element. To perform this latter computation, you must explicitly set the global sample prior to performing the calculation performing the assignment: smpl 1945:01 1979:12 a(1) = @mean(x) To determine the number of observations available for a given series, use the @obs function. Note that where appropriate, EViews will perform casewise exclusion of data with missing @pmt(r,n,pv[,fv,t]) payment amount payment amount for an -period, present value annuity with rate and optional final lump sum payment . A non-zero value for the optional indicates that the payments are made at the beginning of periods. @rate(n,x,pv[,fv,t]) interest interest rate for an period annu- ity with payments , present value , and optional final lump sum payment . A non-zero value for the optional indicates that the payments are made at the beginning of periods. n pv r fv t n x pv fv t
- 560. Descriptive Statistics—553 values. For example, @cov(x,y) and @cor(x,y) will use only observations for which data on both X and Y are valid. In the following table, arguments in square brackets [ ] are optional arguments: • [s]: sample expression in double quotes or name of a sample object. The optional sample argument may only be used if the result is assigned to a series. For @quantile, you must provide the method option argument in order to include the optional sample argument. If the desired sample expression contains the double quote character, it may be entered using the double quote as an escape character. Thus, if you wish to use the equivalent of, smpl if name = "Smith" in your @MEAN function, you should enter the sample condition as: series y = @mean(x, "if name=""Smith""") The pairs of double quotes in the sample expression are treated as a single double quote. Function Name Description @cor(x,y[,s]) correlation the correlation between X and Y. @cov(x,y[,s]) covariance the covariance between X and Y (division by ). @covp(x,y[,s]) population covari- ance the covariance between X and Y (division by ). @covs(x,y[,s]) sample covariance the covariance between X and Y (division by ). @gmean(x[,s]) geometric mean the geometric mean of X. The geo- metric mean is calculated as the exponential of the sum of the logs of X. @hmean(x[,s]) harmonic mean computes the harmonic mean of the values of X. The harmonic mean is calculated as the reciprocal of the mean of the reciprocals of X. @imax(x) maximum index workfile index of the maximum of the values in X for the current sam- ple. @imin(x) minimum index workfile index of the maximum of the values in X for the current sam- ple. @inner(x,y[,s]) inner product the inner product of X and Y. @kurt(x[,s]) kurtosis kurtosis of values in X. n n n 1–
- 561. 554—Chapter 13. Operator and Function Reference @mae(x,y[,s]) mean absolute error the mean of the absolute value of the difference between X and Y. @mape(x,y[,s]) mean absolute per- centage error 100 multiplied by the mean of the absolute difference between X and Y, divided by Y. @max(x[,s]) maximum maximum of the values in X. @maxes(x,n[,s]) n-largest numbers maximum n values in X, arranged largest to smallest, returned in a vec- tor object. Note this function may not be used for series generation. @mean(x[,s]) mean average of the values in X. @median(x[,s]) median computes the median of the X (uses the average of middle two observa- tions if the number of observations is even). @min(x[,s]) minimum minimum of the values in X. @mins(x,n[,s]) n-smallest numbers minimum n values in X, arranged smallest to largest, returned in a vec- tor object. Note this function may not be used for series generation. @nas(x[,s]) number of NAs the number of missing observations for X in the current sample. @prod(x[,s]) product the product of the elements of X (note this function is prone to numerical overflows). @obs(x[,s]) number of observa- tions the number of non-missing observa- tions for X in the current sample. @quantile(x,q[,m,s]) quantile the q-th quantile of the series X. m is an optional string argument for specifying the quantile method: “b” (Blom), “r” (Rankit-Cleveland), “o” (Ordinary), “t” (Tukey), “v” (van der Waerden), “g” (Gumbel). The default value is “r”.
- 562. Descriptive Statistics—555 @ranks(x[,o,t,s]) ranks the ranking of each observation in X. The order of ranking is set using o: “a” (ascending - default) or “d” (descending). Ties are broken according to the set- ting of t: “i” (ignore), “f” (first), “l” (last), “a” (average - default), “r” randomize. If you wish to specify tie-handling options, you must also specify the order option (e.g. ‘@ranks(x, “a”, “i”)’). @rmse(x,y[,s]) root mean square error the square root of the mean of the squared difference between X and Y. @skew(x[,s]) skewness skewness of values in X. @smape(x,y[,s]) symmetric mean absolute percentage error 200 multiplied by the mean of the absolute difference between X and Y divided by the sum of the absolute values of X and Y. @stdev(x[,s]) standard deviation square root of the unbiased sample variance (sum-of-squared residuals divided by ). @stdevp(x[,s]) population standard deviation square root of the population vari- ance (sum-of-squared residuals divided by ). @stdevs(x[,s]) sample standard deviation square root of the unbiased sample variance. Note this is the same cal- culation as @stdev. @sum(x[,s]) sum the sum of X. @sumsq(x[,s]) sum-of-squares sum of the squares of X. @theil(x,y[,s]) Theil inequality coef- ficient the root mean square error divided by the sum of the square roots of the means of X squared and Y squared. @var(x[,s]) variance variance of the values in X (division by ). @varp(x[,s]) population variance variance of the values in X. Note this is the same calculation as @var. @vars(x[,s]) sample variance sample variance of the values in X (division by ). n 1– n n n 1–
- 563. 556—Chapter 13. Operator and Function Reference Cumulative Statistic Functions These functions perform basic running or cumulative statistics for a series and may be used as part of a series expression. The functions are split into two types, those that cumulate for- wards and those that cumulate backwards. The forwards cumulating functions return the running values of a statistic from the start of the workfile (or optionally a sample) to the cur- rent observation. The backwards cumulating functions return the running values from the end of the workfile (or sample) to the current observation. By default, EViews will use the entire workfile range when computing the statistics. You may provide the optional sample s as a literal (quoted) sample expression or a named sample. Missing values, NAs, do not propagate through these functions. Thus the cumulative sums of the numbers 1, 3, 4, NA, 5 are 1, 4, 8, 8, 13. Name Function Description @cumsum(x[,s]) cumulative sum cumulative sum of the values in X from the start of the workfile/sample. @cumprod(x[,s]) cumulative product cumulative product of the values in X from the start of the workfile/sample (note this function could be subject to numerical overflows). @cummean(x[,s]) cumulative mean mean of the values in X from the start of the workfile/sample to the current observation. @cumstdev(x[,s]) cumulative standard devia- tion sample standard deviation of the values in X from the start of the workfile/sam- ple to the current observation. Note this calculation involves division by . @cumstdevp(x[,s]) cumulative population stan- dard deviation population standard deviation of the values in X from the start of the work- file/sample to the current observation. Note this calculation involves division by . @cumstdevs(x[,s]) cumulative sample standard deviation sample standard deviation of the values in X from the start of the workfile/sam- ple. Note this performs the same calcu- lation as @cumstdev. @cumvar(x[,s]) cumulative variance population variance of the values in X from the start of the workfile/sample to the current observation. Note this calcu- lation involves division by . n 1– n n
- 564. Cumulative Statistic Functions—557 @cumvarp(x[,s]) cumulative population vari- ance population variance of the values in X from the start of the workfile/sample to the current observation. Note this per- forms the same calculation as @cumvar. @cumvars(x[,s]) cumulative sample variance sample variance of the values in X from the start of the workfile/sample to the current observation. Note this calcula- tion involves division by . @cummax(x[,s]) cumulative maximum maximum of the values in X from the start of the workfile/sample to the cur- rent observation. @cummin(x[,s]) cumulative minimum minimum of the values in X from the start of the workfile/sample to the cur- rent observation. @cumsumsq(x[,s]) cumulative sum-of-squares sum of squares of the values in X from the start of the workfile/sample to the current observation. @cumobs(x[,s]) cumulative nmber of non- NA observations the number of non-missing observations in X from the start of the workfile/sam- ple to the current observation. @cumnas(x[,s]) cumulative number of NA observations the number of missing observations in X from the start of the workfile/sample to the current observation. @cumquan- tile(x,q[,s]) cumulative quantile the qth quantile in X computed from the start of the workfile/sample to the cur- rent observation. Quantiles are com- puted using the Cleveland definition. @cummedian(x[,s]) cumulative median the median of X computed from the end of the workfile/sample to the current observation. @cumbsum(x[,s]) backwards cumulative sum cumulative sum of the values in X from the end of the workfile/sample. @cumbprod(x[,s]) backwards cumulative product cumulative product of the values in X from the end of the workfile/sample (note this function could be subject to numerical overflows). @cumbmean(x[,s]) backwards cumulative mean mean of the values in X from the end of the workfile/sample to the current observation. @cumbstdev(x[,s]) backwards cumulative stan- dard deviation sample standard deviation of the values in X from the end of the workfile/sam- ple to the current observation. Note this calculation involves division by . n 1– n 1–
- 565. 558—Chapter 13. Operator and Function Reference @cumbstdevp(x[,s]) backwards cumulative pop- ulation standard deviation population standard deviation of the values in X from the end of the work- file/sample to the current observation. Note this calculation involves division by . @cumbstdevs(x[,s]) backwards cumulative sam- ple standard deviation sample standard deviation of the values in X from the end of the workfile/sam- ple. Note this performs the same calcu- lation as @cumstdev. @cumbvar(x[,s]) backwards cumulative vari- ance population variance of the values in X from the end of the workfile/sample to the current observation. Note this calcu- lation involves division by @cumbvarp(x[,s]) backwards cumulative pop- ulation variance population variance of the values in X from the end of the workfile/sample to the current observation. Note this per- forms the same calculation as @cumvar. @cumbvars(x[,s]) backwards cumulative sam- ple variance sample variance of the values in X from the end of the workfile/sample to the current observation. Note this calcula- tion involves division by . @cumbmax(x[,s]) backwards cumulative max- imum maximum of the values in X from the end of the workfile/sample to the cur- rent observation. @cumbmin(x[,s]) backwards cumulative min- imum minimum of the values in X from the end of the workfile/sample to the cur- rent observation. @cumbsumsq(x[,s]) backwards cumulative sum- of-squares sum of squares of the values in X from the start of the workfile/sample to the current observation. @cumbobs(x[,s]) backwards cumulative nmber of non-NA observa- tions the number of non-missing observations in X from the end of the workfile/sam- ple to the current observation. @cumbnas(x[,s]) backwards cumulative nmber of NA observations the number of missing observations in X from the end of the workfile/sample to the current observation. @cumbquan- tile(x,q[,s]) backwards cumulative quantile the qth quantile in X computed from the end of the workfile/sample to the cur- rent observation. Quantiles are com- puted using the Cleveland definition. @cumbmedian(x[,s]) backwards cumulative median the median of X computed from the end of the workfile/sample to the current observation. n n n 1–
- 566. Moving Statistic Functions—559 Moving Statistic Functions These functions perform basic rolling or moving statistics. They may be used as part of a series expression. The moving statistic functions are in two types, those that propagate missing observations (NAs) and those that don’t. The functions that do not propagate NAs, which start with “@m”, skip observations which are NA. The functions that do propagate NAs will return NA when an NA is encountered. For example if the series X contains {1, 3, 4, NA, 5, 3, 2} then “@movav(x,2)” will give {NA, 2, 3.5, NA, NA, 4, 2.5}, whereas “@mav(x,2)” will give {1, 2, 3.5, 4, 5, 4, 2.5}. Name Function Description @movsum(x,n) n-period backward moving sum . NAs are propagated. @movav(x,n) n-period backward moving aver- age NAs are propagated. @movavc(x,n) n-period centered moving aver- age centered moving average of X. Note if n is even then the window length is increased by one and the two endpoints are weighted by 0.5. NAs are propagated. @movstdev(x,n) n-period backwards moving stan- dard deviation sample standard deviation (divi- sion by ) of X for the current and previous observations. NAs are propagated. @movstdevs(x,n) n-period backwards moving sam- ple standard deviation sample standard deviation (divi- sion by ) of X for the current and previous observations. Note this is the same calculation as @movstdev. NAs are propagated. @movstdevp(x,n) n-period backwards moving pop- ulation standard deviation population standard deviation (division by ) of X for the current and previous observations. NAs are propagated. @movvar(x,n) n-period backwards moving vari- ance population variance (division by ) of X for the for the current and previous observations. NAs are propagated. @movsum(x,3) X X 1– X 2– @movav(x, 3) X X 1– X 2– 3 n 1– n 1– n 1– n 1– n n 1– n n 1–
- 567. 560—Chapter 13. Operator and Function Reference @movvars(x,n) n-period backwards moving sam- ple variance sample variance (division by ) of X for the current and previous observations. NAs are propagated. @movvarp(x,n) n-period backwards moving pop- ulation variance population variance (division by ).of X for the current and previ- ous observations. Note this is the same calculation as @mov- var. NAs are propagated. @movcov(x,y,n) n-period backwards moving covariance population covariance (division by ) between X and Y of the current and previous observations. NAs are propagated. @movcovs(x,y,n) n-period backwards moving sam- ple covariance sample covariance (division by ) between X and Y of the current and previous obser- vations. NAs are propagated. @movcovp(x,y,n) n-period backwards moving pop- ulation covariance population covariance (division by ) between X and Y of the current and previous observations. Note this is the same calculation as @movcov. NAs are propagated. @movcor(x,y,n) n-period backwards moving cor- relation correlation between X and Y of the current and previous obser- vations. NAs are propagated. NAs are propagated. @movmax(x,n) n-period backwards moving max- imum the maximum of X for the current and previous observations. NAs are propagated. @movmin(x,n) n-period backwards moving min- imum the minimum of X for the current and previous observations. NAs are propagated. @movsumsq(x,n) n-period backwards sum-of- squares the sum-of-squares of X for the current and previous obser- vations. NAs are propagated. @movskew(x,n) n-period backwards skewness the skewness of X for the current and previous observations. NAs are propagated. @movkurt(x,n) n-period backwards kurtosis the kurtosis of X for the current and previous observations. NAs are propagated. n 1– n 1– n n 1– n n 1– n 1– n 1– n n 1– n 1– n 1– n 1– n 1– n 1– n 1–
- 568. Moving Statistic Functions—561 @movobs(x,n) n-period backwards nmber of non-NA observations the number of non-missing obser- vations in X for the current and previous observations. Note this function always returns the same value as @mobs. @movnas(x,n) n-period backwards nmber of NA observations the number of missing observa- tions in X for the current and previ- ous n-1 observations. Note this function always returns the same value as @mnas. @movinner(x,y,n) n-period backwards inner prod- uct of X and Y inner product of X and Y for the current and previous obser- vations. NAs are propagated. @msum(x,n) n-period backward moving sum NAs are not propagated. @mav(x,n) n-period backward moving aver- age NAs are not propagated. @mavc(x,n) n-period centered moving aver- age centered moving average of X. Note if n is even then the window length is increased by one and the two endpoints are weighted by 0.5. NAs are not propagated. @mstdev(x,n) n-period backwards moving stan- dard deviation sample standard deviation (divi- sion by ) of X for the current and previous observations. NAs are not propagated. @mstdevs(x,n) n-period backwards moving sam- ple standard deviation sample standard deviation (divi- sion by ) of X for the current and previous observations. Note this is the same calculation as @movstdev. NAs are not propa- gated. @mstdevp(x,n) n-period backwards moving pop- ulation standard deviation population standard deviation (division by ) of X for the current and previous observations. NAs are not propagated. @mvar(x,n) n-period backwards moving vari- ance population variance (division by ) of X for the for the current and previous observations. NAs are not propagated. n 1– n 1– @msum(x,3) X X 1– X 2– @mav(x, 3) X X 1– X 2– 3 n 1– n 1– n 1– n 1– n n 1– n n 1–
- 569. 562—Chapter 13. Operator and Function Reference @mvars(x,n) n-period backwards moving sam- ple variance sample variance (division by ) of X for the current and previous observations. NAs are not propagated. @mvarp(x,n) n-period backwards moving pop- ulation variance population variance (division by ) of X for the current and previ- ous observations. Note this is the same calculation as @mov- var. NAs are not propagated. @mcov(x,y,n) n-period backwards moving covariance population covariance (division by ) between X and Y of the current and previous observations. NAs are not propagated. @mcovs(x,y,n) n-period backwards moving sam- ple covariance sample covariance (division by ) between X and Y of the current and previous obser- vations. NAs are not propagated. @mcovp(x,y,n) n-period backwards moving pop- ulation covariance population covariance (division by ) between X and Y of the current and previous observations. Note this is the same calculation as @movcov. NAs are not propagated. @mcor(x,y,n) n-period backwards moving cor- relation correlation between X and Y of the current and previous obser- vations. NAs are not propagated. @mmax(x,n) n-period backwards moving max- imum the maximum of X for the current and previous observations. NAs are not propagated. @mmin(x,n) n-period backwards moving min- imum the minimum of X for the current and previous observations. NAs are not propagated. @msumsq(x,n) n-period backwards sum-of- squares the sum-of-squares of X for the current and previous obser- vations. NAs are not propagated. @mskew(x,n) n-period backwards skewness the skewness of X for the current and previous observations. NAs are not propagated. @mkurt(x,n) n-period backwards kurtosis the kurtosis of X for the current and previous observations. NAs are not propagated. n 1– n 1– n n 1– n n 1– n 1– n 1– n n 1– n 1– n 1– n 1– n 1– n 1– n 1–
- 570. Group Row Functions—563 Group Row Functions These functions work on a group object. They may be used to create a new series where each row of that series is a function of the corresponding row of a group. Thus, if group G contains the series X, Y and Z, the @rmean function will return (X+Y+Z)/3. These functions do not propagate NAs, i.e., they generate data based upon the observations within the group row that are non-NA. As an example, the @rsum function will return a series where each row is equal to the sum of the non-NA elements in the row of a group. Note that in the following descriptions, G is a named group in your workfile. @mobs(x,n) n-period backwards number of non-NA observations the number of non-missing obser- vations in X for the current and previous observations. Note this function always returns the same value as @movobs. @mnas(x,n) n-period backwards number of NA observations the number of missing observa- tions in X for the current and previ- ous observations. Note this function always returns the same value as @movnas. @minner(x,y,n) n-period backwards inner prod- uct of X and Y inner product of X and Y for the current and previous obser- vations. NAs are not propagated. Name Function Description @columns(g) number of columns. returns the number of columns in G. Note this is the same as “G.@count”, and will return the same number for every row. @rnas(g) row-wise number of NAs. the number of missing observa- tions in the rows of G. @robs(g) row-wise number of non-NAs. the number of non-missing obser- vations in the rows of G. @rvalcount(g,v) row-wise count of observations matching v. the number of observations with a value equal to v in the rows of G. Note v can be a scalar or a series @rsum(g) row-wise sum. the sum of the rows of G. @rmean(g) row-wise mean. the mean of the rows of G. n 1– n 1– n 1–
- 571. 564—Chapter 13. Operator and Function Reference @rstdev(g) row-wise standard deviation. the sample standard deviation of the rows of G. Note division is by . @rstdevp(g) row-wise population standard deviation. the population standard deviation of the rows of G. Note division is by . @rstdevs(g) row-wise sample standard devia- tion. the sample standard deviation of the rows of G. Note division is by , and this calculation is the same as @rstdev. @rvar(g) row-wise variance. the population variance of the rows of G. Note division is by . @rvarp(g) row-wise population variance the population variance of the rows of G. Note division is by , and this calculation is the same as @rvar. @rvars(g) row-wise sample variance. the sample variance of the rows of G. Note division is by . @rsumsq(g) row-wise sum-of-squares. the sum of the squares of the rows of G. @rfirst(g) row-wise first non-NA value. the value of the first non-missing observation in the rows of G. @rifirst(g) row-wise first non-NA index. the index (i.e., column number) of the first non-missing observation in the rows of G. @rilast(g) row-wise last non-NA index. the index (i.e., column number) of the last non-missing observation in the rows of G. @rimax(g) row-wise maximum index. the index (i.e., column number) of the maximum observation in the rows of G. In the case of ties, the first matching index is given. @rimin(g) row-wise minimum index. the index (i.e., column number) of the minimum observation in the rows of G. In the case of ties, the first matching index is given. @rlast(g) row-wise last non-NA value. the value of the last non-missing observation in the rows of G @rmax(g) row-wise maximum value. the value of the maximum obser- vation in the rows of G. @rmin(g) row-wise minimum value. the value of the minimum observa- tion in the rows of G. n 1– n n 1– n n n 1–
- 572. By-Group Statistics—565 By-Group Statistics The following “by group”-statistics are available in EViews. They may be used as part of a series expression statements to compute the statistic for subgroups, and to assign the value of the relevant statistic to each observation. The functions take a series expression arg1 for which we wish to compute group statistics and one or more series expressions arg2, defining the groups over which we wish to com- pute the statistics. In the leading case, you will provide a single series expression containing categories. More generally, you can provide several series expressions separated by commas (arg2, arg3, ...) that jointly define the categories. For brevity, the table entries below only depict a single categorical variable arg2. Function Description @obsby(arg1, arg2[, s]) Number of non-NA arg1 observations for each arg2 group. @nasby(arg1, arg2[, s]) Number of arg1 NA values for each arg2 group. @sumsby(arg1, arg2[, s]) Sum of arg1 observations for each arg2 group. @meansby(arg1, arg2[, s]) Mean of arg1 observations for each arg2 group. @minsby(arg1, arg2[, s]) Minimum value of arg1 observations for each arg2 group. @maxsby(arg1, arg2[, s]) Maximum value of arg1 observations for each arg2 group. @mediansby(arg1, arg2[, s]) Median of arg1 observations for each arg2 group. @varsby(arg1, arg2[, s]) Variance of arg1 observations for each arg2 group (division by ). @varssby(arg1, arg2[, s]) Sample variance of arg1 observations for each arg2 group (division by ). @varpsby(arg1, arg2[, s]) Variance of arg1 observations for each arg2 group (division by ). @stdevsby(arg1, arg2[, s]) Sample standard deviation of arg1 observations for each arg2 group (division by ). @stdevssby(arg1, arg2[, s]) Sample standard deviation of arg1 observations for each arg2 group (division by ). @stdevpsby(arg1, arg2[, s]) Standard deviation of arg1 observations for each arg2 group (division by ). @sumsqsby(arg1, arg2[, s]) Sum of squares of arg1 observations for each arg2 group. @quantilesby(arg1, arg2, arg3[, s]) Quantiles of arg1 observations for each arg2 group where arg1 is the desired quantile. n n 1– n n 1– n 1– n
- 573. 566—Chapter 13. Operator and Function Reference With the exception of @quantileby, all of the functions take the form: @STATBY(arg1, arg2[, arg3, arg4, ..., argn, s]) where @STATBY is one of the by-group function keyword names, arg1 is a series expression, arg2 and the optional arg3 to argn are numeric or alpha series expression identifying the subgroups, and s is an optional sample literal (a quoted sample expression) or a named sam- ple. With the exception of @obsby, arg1 must be a numeric series. By default, EViews will use the workfile sample when computing the descriptive statistics. You may provide the optional sample s as a literal (quoted) sample expression or a named sample. The @quantileby function requires an additional argument for the quantile value that you wish to compute: @quantileby(arg1, arg2[, arg3, arg4, ..., argn], q[, s]) For example, to compute the first quartile, you should use the q value of 0.25. There are two related functions of note, @groupid(arg1[, arg2, arg3, arg4, ..., argn, smpl]]) returns an integer indexing the group ID for each observation of the series or alpha expres- sion arg. Special Functions EViews provides a number of special functions used in evaluating the properties of various statistical distributions or for returning special mathematical values such as Euler’s con- stant. For further details on special functions, see the extensive discussions in Temme (1996), Abramowitz and Stegun (1964), and Press, et al. (1992). @skewsby(arg1, arg2[, s]) Skewness of arg1 observations for each arg2 group. @kurtsby(arg1, arg2[, s]) Kurtosis of arg1 observations for each arg2 group. @firstsby(arg1, arg2[, s]) First non-missing value in arg1 for each arg2 group @lastsby(arg1, arg2[, s]) Last non-missing value in arg1 for each arg2 group Function Description @beta(a,b) beta integral (Euler integral of the second kind): for . B a b, t a 1– 1 t– b 1– td 0 1 G a G b G a b ------------------------ a b 0,
- 574. Special Functions—567 @betainc(x,a,b) incomplete beta integral: for and . @betaincder(x,a,b,s) derivative of the incomplete beta integral: evaluates the derivatives of the incomplete beta integral , where is an integer from 1 to 9 correspond- ing to the desired derivative: @betaincinv(p,a,b) inverse of the incomplete beta integral: returns an satis- fying: for and . @betalog(a,b) natural logarithm of the beta integral: . @binom(n,x) binomial coefficient: for and positive integers, . @binomlog(n,x) natural logarithm of the binomial coefficient: @cloglog(x) complementary log-log function: See also @qextreme. @digamma(x), @psi(x) first derivative of the log gamma function: 1 B a b, ------------------ t a 1– 1 t– b 1– td 0 x 0 x 1 a b 0, B x a b, , s 1 2 3 4 5 6 7 8 9 B x ------- B a ------- B b ------- 2 B x 2 ---------- 2 B xa ------------- 2 B xb ------------ 2 B a 2 ---------- 2 B ab ------------- 2 B b 2 ---------- x p 1 B a b, ------------------ t a 1– 1 t– b 1– td 0 x 0 p 1 a b 0, B a b, log G a log G b log G a b log– n x n! x! n x– ! ------------------------- n x 0 x n n! log x! log– n x– ! log– 1 x– log– log w x d G x log dx ----------------------- 1 G x ----------- dG x dx ---------------
- 575. 568—Chapter 13. Operator and Function Reference @erf(x) error function: for . @erfc(x) complementary error function: . for . @gamma(x) (complete) gamma function: for . @gammader(x) first derivative of the gamma function: Note: Euler’s constant, , may be evaluated as . See also @digamma and @tri- gamma. @gammainc(x,a) incomplete gamma function: for and . @gammaincder(x,a,n) derivative of the incomplete gamma function: Evaluates the derivatives of the incomplete gamma integral , where is an integer from 1 to 5 corresponding to the desired derivative: @gammaincinv(p,a) inverse of the incomplete gamma function: find the value of satisfying: for and . erf x 2 p ------- e t 2 – td 0 x x 0 erfc x 2 p ------- e t 2 – td x 1 erf x – x 0 G x e t– t x 1– td 0 x 0 G x dG x dx g 0.5772 g @gammader(1)– G x a, 1 G a ------------ e t– t a 1– td 0 x x 0 a 0 G x a, n 1 2 - 3 4 5 G x -------- G a -------- - 2 G x 2 ---------- 2 G xa ------------- 2 G a 2 ---------- x p G x a, 1 G a ------------ e t– t a 1– td 0 x 0 p 1 a 0
- 576. Statistical Distribution Functions—569 Trigonometric Functions When applied to a series, all of the trigonometric functions operate on every observation in the current sample and return a value for every observation. Where relevant, the input and results should/will be expressed in radians. All results are real valued—complex values will return NAs. Statistical Distribution Functions The following functions provide access to the density or probability functions, cumulative distribution, quantile functions, and random number generators for a number of standard statistical distributions. There are four functions associated with each distribution. The first character of each func- tion name identifies the type of function: @gammalog(x) logarithm of the gamma function: . For deriva- tives of this function see @digamma and @trigamma. @logit(x) logistic transform: @psi(x) see @digamma. @trigamma(x) second derivative of the log gamma function: Function Name Examples/Description @acos(x) arc cosine (real results in radians) @asin(x) arc sine (real results in radians) @atan(x) arc tangent (results in radians) @cos(x) cosine (argument in radians) @sin(x) sine (argument in radians) @tan(x) tangent (argument in radians) Function Type Beginning of Name Cumulative distribution (CDF) @c Density or probability @d Quantile (inverse CDF) @q G x log 1 1 e x– ----------------- e x 1 e x -------------- w x d 2 G x log dx 2 -------------------------- @acos(-1) p @asin(-1) p 2 @atan(1) p 4 @cos(3.14159) 1– @sin(3.14159) 0 @tan(1) 1.5574
- 577. 570—Chapter 13. Operator and Function Reference The remainder of the function name identifies the distribution. For example, the functions for the beta distribution are @cbeta, @dbeta, @qbeta and @rbeta. When used with series arguments, EViews will evaluate the function for each observation in the current sample. As with other functions, NA or invalid inputs will yield NA values. For values outside of the support, the functions will return zero. Note that the CDFs are assumed to be right-continuous: . The quantile functions will return the smallest value where the CDF evaluated at the value equals or exceeds the probability of interest: , where . The inequalities are only relevant for discrete distributions. The information provided below should be sufficient to identify the meaning of the parame- ters for each distribution. Random number generator @r Distribution Functions Density/Probability Function Beta @cbeta(x,a,b), @dbeta(x,a,b), @qbeta(p,a,b), @rbeta(a,b) for and for , where is the @beta function. Binomial @cbinom(x,n,p), @dbinom(x,n,p), @qbinom(s,n,p), @rbinom(n,p) if , and 0 otherwise, for . Chi-square @cchisq(x,v), @dchisq(x,v), @qchisq(p,v), @rchisq(v) where , and . Note that the degrees of freedom parameter need not be an integer. In addition, the @chisq(x,v) function may be used to obtain the p-values directly. Exponential @cexp(x,m), @dexp(x,m), @qexp(p,m), @rexp(m) for , and . Extreme Value (Type I-minimum) @cextreme(x), @dextreme(x), @qextreme(p), @cloglog(p), @rextreme for . FX k Pr X k qX p q FX q p f x a b, , x a 1– 1 x– b 1– B a b, -------------------------------------- 0 p 1 a b, 0 B Pr x n p, , n x p x 1 p– n x– x 0 1 n , , , , 0 p 1 f x v, 1 2 v 2 G v 2 ------------------------------x v 2 1– e x 2– x 0 v 0 v f x m, 1 m ----e x m– x 0 m 0 f x x e x – exp x –
- 578. Statistical Distribution Functions—571 F-distribution @cfdist(x,v1,v2), @dfdist(x,v1,v2), @qfdist(p,v1,v2), @rfdist(v1,v1) where , and . Note that the functions allow for fractional degrees of free- dom parameters and . Gamma @cgamma(x,b,r), @dgamma(x,b,r), @qgamma(p,b,r), @rgamma(b,r) where , and . Generalized Error @cged(x,r), @dged(x,r), @qged(p,r), @rged(r) where , and . Laplace @claplace(x), @dlaplace(x), @qlaplace(x), @rlaplace for . Logistic @clogistic(x), @dlogistic(x), @qlogistic(p), @rlogistic for . Log-normal @clognorm(x,m,s), @dlognorm(x,m,s), @qlognorm(p,m,s), @rlognorm(m,s) , , and . Negative Binomial @cnegbin(x,n,p), @dnegbin(x,n,p), @qnegbin(s,n,p), @rnegbin(n,p) if , and 0 otherwise, for . Normal (Gaussian) @cnorm(x), @dnorm(x), @qnorm(p), @rnorm, nrnd for . Poisson @cpoisson(x,m), @dpoisson(x,m), @qpoisson(p,m), @rpoisson(m) if , and 0 otherwise, for . f x v1 v2, , v1 v1 2 v2 v2 2 B v1 2 v2 2, -------------------------------------- x v1 2– 2 v2 v1x v1 v2 2– x 0 v1 v2, 0 v1 v2 f x b r, , b r– x r 1– e x b– G r x 0 b r 0, f x r, rG 3 r --- 1 2 2G 1 r --- 3 2 ------------------------ x r G 3 r --- G 1 r --- ------------- r 2 – exp x – r 0 f x 1 2 ---e x– x – f x 1 1 e x– ----------------- e x 1 e x -------------- x – f x m s, , 1 x 2ps 2 -------------------e x m–log 2 2s 2 – x 0 0 m s 0 Pr x n p, , G x n G x 1 G n ----------------------------------p n 1 p– x x 0 1 n , , , , 0 x 1 f x 2p 1 2– e x 2 2– x – Pr x m, m x e m– x! x 0 1 n , , , , m 0
- 579. 572—Chapter 13. Operator and Function Reference Additional Distribution Related Functions The following utility functions were designed to facilitate the computation of p-values for common statistical tests. While these results may be derived using the distributional func- tions above, they are retained for convenience and backward compatibility. Pareto @cpareto(x,k,a), @dpareto(x,k,a), @qpareto(p,k,a), @rpareto(k,a) for location parameter and shape parameter . Student's -distribution @ctdist(x,v), @dtdist(x,v), @qtdist(p,v), @rtdist(v) for , and . Note that , yields the Cauchy distribution. Uniform @cunif(x,a,b), @dunif(x,a,b), @qunif(p,a,b), @runif(a,b), rnd for and . Weibull @cweib(x,m,a), @dweib(x,m,a), @qweib(p,m,a), @rweib(m,a) where , and . Function Distribution Description @chisq(x,v) Chi-square Returns the probability that a Chi-squared sta- tistic with degrees of freedom exceeds : @chisq(x,v)=1–@cchisq(x,d) @fdist(x,v1,v2) F-distribution Probability that an F-statistic with numer- ator degrees of freedom and denominator degrees of freedom exceeds : @fdist(x,v1,v2)=1–@cfdist(x,v1,v2) @tdist(x,v) t-distribution Probability that a t-statistic with degrees of freedom exceeds in absolute value (two- sided p-value): @tdist(x,v)=2*(1–@ctdist(@abs(x),v)) f x k a, , ak a x a 1 0 k x a 0 t f x v, G v 1 2 ------------ vp 1 2 --- G v 2 --- --------------------------- 1 x 2 v ----- v 1 – 2 -------------------- x – v 0 v 1 f x 1 b a– ------------ a x b b a f x m a, , am a– x a 1– e x m a – 0 x m a 0, v x v1 v2 x v x
- 580. Workfile & Informational Functions—573 String Functions EViews offers an extensive library of functions for working with strings. For a complete list of relevant functions, see “String Function Summary,” on page 611. For discussion, see “Strings,” on page 77. Date Functions EViews provides a powerful collection of functions for working with dates. For a list of func- tions see “Date Function Summary,” on page 612. For discussion, see “Dates,” on page 94. Indicator Functions These functions produce indicators for whether each observation satisfies a specific condi- tion: Workfile & Informational Functions The following list summarizes the utility functions that are designed to provide information about the currently active workfile and the EViews environment. These functions are described in greater detail in Chapter 15. “Workfile Functions,” on page 593. General Information Function Description @inlist(series, "list") Creates a dummy variable equal to 1 for observa- tions where series is equal to one of the values specified in list, and 0 otherwise. list should be a quoted, space delimited list of values. This function works on both numerical and alpha series. @between(series, val1, val2) Creates a dummy variable equal to 1 for observa- tions where series is greater than or equal to val1 and less than or equal to val2. Function Name Description @env(str) environment variables returns a string containing the value of the Windows environment vari- able str. For example @env(“username”) returns the user-name of the current logged in user. @env(“computer- name”) returns the name of the computer.
- 581. 574—Chapter 13. Operator and Function Reference Element Information @fileexist(str) file exist returns a 0 or 1 depending on whether the filename specified by str exists on disk. @folderex- ist(str) folder exist returns a binary scalar, equal to 1 if the folder specified by str exists on disk, and 0 otherwise. @table- names(str) table/sheet names returns a space delimited string con- taining the names of the table names of a foreign file. str should be the name (and path) of the foreign file, enclosed in quotes. For an Excel file, the names of the Excel sheets will be returned. @getnext- name(str) get next name returns a string containing the next available variable name in the work- file, starting with str. e.g. entering “result” will return “result01” unless there is already a RESULT01, in which case it will return “result02”. @vernum version number returns a scalar containing the EViews version number. @verstr version string returns a string containing the EViews version string (e.g. “EViews Enterprise Edition”). @wdir(str) directory listing returns a string list containing a list of all of the files in the directory str. @wquery(“data- base”, “search_ex- pression”, “attribute_list”) database query returns a string list containing the attribute_list of all objects in data- base that match the query given by search_expression (see @wquery (p. 737) for details). Function Name Description @elem(x,"v") element returns the value of a series at an observation or date. The observation or date should be entered in double quotes. @first(x) first element returns the value of the first non- missing value in the series for the current sample.
- 582. Workfile & Informational Functions—575 Basic Workfile Functions @ifirst(x) index of first element returns the workfile index of the first non-missing value in the series for the current sample. @ilast(x) last element returns the workfile index of the last non-missing value in the series for the current sample. @last(x) index of last element returns the value of the last non- missing value in the series for the current sample. Function Name Description @ispanel panel workfile returns a 0 or 1 depending on whether the workfile is panel struc- tured. @obsrange observations in range returns the number of observations in the workfile range. @obssmpl observations in sample returns the number of observations in the workfile sample. @pagecount page count returns a scalar containing the num- ber of pages in the current workfile. @pageexist(str) page exists returns a 0 or 1 depending on whether the page specified by str exists in the current workfile. @pagefreq page frequency returns a string containing the fre- quency of the active page. @pageids workfile page identifiers returns a string containing a space delimited list of the id series for the current workfile page. @pagelist page names list returns a space delimited string con- taining the names of all the pages in the current active workfile. @pagename page name returns a string containing the name of the active page. @pagesmpl workfile page sample returns a string containing the cur- rent sample for the active page. @wfname workfile name returns a string containing the cur- rent default workfile name. @wfpath workfile path returns a string containing the cur- rent default workfile path.
- 583. 576—Chapter 13. Operator and Function Reference Dated Workfile Information The following functions return information about each observation in the workfile. They are used to generate new series. Function Name Description @after(arg1) date is after creates a dummy variable equal to 1 if the observation is after or on the date given by arg1. arg1 should be enclosed in quotes. @before(arg1) date is before creates a dummy variable equal to 1 if the observation is before the date given by arg1. arg1 should be enclosed in quotes. @date date returns the date value. @day day of month returns the day of the month. @during(arg1 date is during creates a dummy variable equal to 1 if the observation lies between the dates given by the date pair con- tained in arg1, and 0 otherwise. arg1 should be given in quotes. @enddate observations in range returns the end date of the period of time associated with the obser- vation in the workfile. @event(arg1[, basis]) event proportion proportion of a one-off event that lies in each observation. @holiday(arg1[, basis]) holiday proportion proportion of an annual event that lies in each observation. @hour hour returns the hour of each observation as an integer. @hourf hour floating point returns the hour of each observation as a floating point number (e.g., 9:30 a.m. returns 9.5, and 5:15 p.m. returns 17.25). @isperiod(x) period dummy returns a dummy variable for whether the observation is in the specified period. @minute minutes returns the minute of each observa- tion as an integer. @month month returns the month of an observation. @quarter quarter returns the quarter of an observa- tion.
- 584. Valmap Functions—577 Panel Workfile Functions Valmap Functions The following utility functions were designed to facilitate working with value mapped series. Additional detail is provided in “Valmap Functions” on page 218 of the User’s Guide I @seas(x) seasonal dummy returns a seasonal dummy variable. @second seconds returns the second of each observa- tion as an integer. @strdate(x) string dates returns a string representing the date for every observation. @trend([x]) time trend returns a time trend using the work- file calendar. @trendc([x]) calendar time trend returns a time trend where the trend uses calendar time. @weekday day of the week returns the day of the week. @year year returns the year Function Name Description @cellid element returns the inner dimension identi- fier index. @crossid observations in range returns the cross-section identifier index. @obsid cross-section observation number returns the observation number within each panel cross section. @trend time trend returns a time trend for each cross- section using the observed dates in the panel. @trendc calendar time trend returns a time trend for each cross- section where the trend uses calen- dar time. Function Name Description @map(x[, mapname]) mapped value returns the mapped values of a series. @unmap(x, mapname) unmapped numeric value returns the numeric values associ- ated with a set of string value labels. @unmaptxt(x, mapname) unmapped text value returns the string values associated with a set of string value labels.
- 585. 578—Chapter 13. Operator and Function Reference References Abramowitz, M. and I. A. Stegun (1964). Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables, New York: Dover Publications. Press, W. H., S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery (1992). Numerical Recipes in C, 2nd edi- tion, Cambridge University Press. Temme, Nico M. (1996). Special Functions: An Introduction to the Classical Functions of Mathematical Physics, New York: John Wiley & Sons.
- 586. Chapter 14. Operator and Function Listing The following reference is an alphabetical listing of operators and functions which may be used in series assignment and generation, and in many cases, in matrix operations or ele- ment evaluation. Additional details on these operators and functions is provided in Chapter 13. “Operator and Function Reference,” on page 547. Operator / Function Description + add – subtract * multiply / divide ^ raise to the power < less than <= less than or equal to <> not equal to = equal to > greater than >= greater than or equal to @abs(x), abs(x) absolute value @acos(x) arc cosine (real results in radians) @after(arg1) Creates a dummy variable equal to 1 if the observation is after or on the date given by arg1. arg1 should be enclosed in quotes. and logical and @asin(x) arc sine (real results in radians) @atan(x) arc tangent (results in radians) @before(arg1) Creates a dummy variable equal to 1 if the observation is before the date given by arg1. arg1 should be enclosed in quotes. @beta(a,b) beta integral @betainc(x,a,b) incomplete beta integral @betaincder(x,a,b,s) derivative of the incomplete beta integral @betaincinv(p,a,b) inverse of the incomplete beta integral @betalog(a,b) natural logarithm of the beta integral
- 587. 580—Chapter 14. Operator and Function Listing @between(x, val1, val2) Creates a dummy variable equal to 1 for observations where the series is greater than or equal to val1 and less than or equal to val2. @binom(n,x) binomial coefficient @binomlog(n,x) natural logarithm of the binomial coefficient @cbeta(x,a,b) beta cumulative distribution (CDF) @cbinom(x,n,p) binomial cumulative distribution (CDF) @cchisq(x,v) chi-square cumulative distribution (CDF) @ceiling(x) smallest integer not less than @cellid element @cexp(x,m) exponential cumulative distribution (CDF) @cextreme(x) extreme value cumulative distribution (CDF) @cfdist(x,v1,v2) F-distribution cumulative distribution (CDF) @cgamma(x,b,r) gamma cumulative distribution (CDF) @cged(x,r) generalized error cumulative distribution (CDF) @chisq(x,v) chi-square p-value @claplace(x) Laplace cumulative distribution (CDF) @clogistic(x) logistic cumulative distribution (CDF) @cloglog(x) complementary log-log function @clognorm(x,m,s) log normal cumulative distribution (CDF) @cnegbin(x,n,p) negative binomial cumulative distribution (CDF) @cnorm(x) normal cumulative distribution (CDF) @columns(g) number of columns. @cor(x,y[,s]) correlation @cos(x) cosine (argument in radians) @cov(x,y[,s]) population covariance @covp(x,y[,s]) population covariance @covs(x,y[,s]) sample covariance @cpareto(x,a,k) Pareto cumulative distribution (CDF) @cpoisson(x,m) Poisson cumulative distribution (CDF) @crossid observations in range @ctdist(x,v) Student’s t-distribution cumulative distribution (CDF) @cumbmax(x[,s]) backwards cumulative maximum @cumbmean(x[,s]) backwards cumulative mean Operator / Function Description
- 588. —581 @cumbmin(x[,s]) backwards cumulative minimum @cumbnas(x[,s]) backwards cumulative nmber of NA observations @cumbobs(x[,s]) backwards cumulative nmber of non-NA observations @cumbprod(x[,s]) backwards cumulative product @cumbstdev(x[,s]) backwards cumulative sample standard deviation @cumbstdevp(x[,s]) backwards cumulative po