Memento on EViews Output Jonathan Benchimol† This version: December 27, 2020 First version: February 10, 2008 Abstract Running a simple least square regression requires to satisfy several hy-potheses. This technical guide explains outputs and interpretations from standard econometric procedures in Eviews. Simple examples and estima. Eviews free download - Q-See eView, E-View7, eView, and many more programs. The new EViews 12 release has introduced several new statistical and econometric procedures. Among them is an engine for wavelet analysis. This is a complement to the existing battery of techniques in EViews used to analyze and isolate features which characterize a time series.

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We get asked questions on dummy variable creation in EViews fairly regularly, so I thought I'd write up a quick all-inclusive guide.
Simple Dummies
The easiest way to create a dummy variable is with the @recode function. @recode lets you specify a logical test, and the values a variable should take if that test is true, or if it is false:

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series dummy1 = @recode(X>0.5, 1, 0)

This will create a series called dummy1 that is equal to 1 whenever the series X is greater than 0.5, and equal to 0 otherwise. You can use the AND or OR operators to make more complicated logical tests:

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series dummy2 = @recode(X>=0.5 and X<=1, 1, 0)

will create a dummy that is equal to 1 whenever X is between 0.5 and less than 1.
Date Dummies
You can use @recode to create dummy variables based upon dates too. In dated workfiles there are a collection of keywords you can use to refer to the date of each observation. The first of these is the @date keyword that returns the date number associated with each observation's date. You can couple this with the @dateval command to create a date number based upon the text representation of a date, in order to create dummy variables based upon dates:

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series dummy3 = @recode(@date>@dateval('2010/03/02'), 1, 0)


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This will create a dummy variable, dummy3 that is equal to 1 for all dates after 2010/03/02.
You can extend this with ANDs and ORs too:

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series dummy4 = @recode(@date>@dateval('2010/03/02') and @date<@dateval('2010/06/02'), 1, 0)

creates a dummy variable equal to 1 between 2010/03/02 and 2010/06/02.

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series dummy5 = @recode(@date<@dateval('1980') or @date>@dateval('1990'), 1, 0)

creates a dummy equal to 1 for all dates before 1980 or after 1990.
You can use other date keywords to create dummies too. The @year specifies the year part of each observations. Thus:

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series dummy6 = @recode(@year>1990, 1, 0)

creates a dummy equal to 1 for all observations which lie after 1990.

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series dummy7 = @recode(@month=1, 1, 0)

creates a dummy equal to 1 for all observations in January.

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series dummy8 = @recode(@weekday=3, 1, 0)

creates a dummy equal to 1 for all observations on a Wednesday.
Dummies in Equations
Note you do not have to actually create the dummy series inside the workfile to use dummy variables in an equation, rather you can enter the dummy expression directly in the equation specification, either via command:

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equation eq1.ls Y C X @date>@dateval('1990')

or via dialog:
Note that in both cases you should not have spaces in the logical expression. @year>1990 is fine, but @year > 1990 is not.
Categorical Dummies
If you have a categorical variable and wish to create dummy variables for each unique values in that variable, you can do so easily using the @expand command. For example, say you have a variable called 'Sex' that is either 'M' or 'F', you could create an equation with the following specification:
which would give the following output:
Note that by default the @expand keyword will create a full set of dummies, thus you should not include a constant in your equation (since you'll create a singular matrix problem).
Alternatively, @expand lets you drop certain values from the expansion to avoid the singularity problem. You can use @dropfirst or @droplast to drop the first or the last categorisation. Thus:
Would regress Y on a constant, X and SEX=M, and not SEX=F.
Fixed Effect Dummies
Fixed effects in panel estimation can be thought of as having a dummy variable for each cross-section. In most cases you don't need to worry about that, since EViews will add the fixed effects for you as an option during estimation. But sometimes you might want to create the dummy variables yourself. To do so is relatively simple, using @expand. Simply include this:

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@expand(@crossid)


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in your specification to include cross-section fixed effects. For period fixed effects, simply include:

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Remember to be careful of the dummy variable trap. You might want to drop one of the dummies, as outlined above.