EViews is a statistical software package designed for Windows mainly. It is used for time-series based data in the field of econometric analysis. Eviews was developed by a company called Quantitative Micro Software which now comes under IHS. The first version was launched in 1994 and since then 10 releases have been made. EViews is a very powerful and flexible software for timeseries modeling and forecasting. It provides a wide range of sophisticated analytical tools which are extremely beneficial for researchers. It’s user interface with menu driven features makes it quite user-friendly. I use EViews very frequently for analytical works.
| Developer(s) | IHS Markit |
|---|---|
| Stable release | |
| Operating system | Windows 7 or newer |
| Type | Econometrics software |
| License | Proprietary |
| Website | www.eviews.com |
EViews is a statistical package for Windows, used mainly for time-series oriented econometric analysis. It is developed by Quantitative Micro Software (QMS), now a part of IHS. Version 1.0 was released in March 1994, and replaced MicroTSP.[1] The TSP software and programming language had been originally developed by Robert Hall in 1965. The current version of EViews is 12, released in November 2020.
Features[edit]
EViews can be used for general statistical analysis and econometric analyses, such as cross-section and panel data analysis and time series estimation and forecasting.
EViews combines spreadsheet and relational database technology with the traditional tasks found in statistical software, and uses a Windows GUI. This is combined with a programming language which displays limited object orientation.
The Enterprise edition of EViews allows access to 3rd party time series data from multiple providers including:[2] Thomson Reuters Datastream, Moody's Economy.com, Macrobond Financial,[3] Haver Analytics,[4] and CEIC.
Data formats[edit]
EViews relies heavily on a proprietary and undocumented file format for data storage. However, for input and output it supports numerous formats, including databank format, Excel formats, PSPP/SPSS, DAP/SAS, Stata, RATS, and TSP. EViews can access ODBC databases. EViews file formats can be partially opened by gretl.
Stationarity of data[edit]
EViews helps researchers detect unit roots in their data series. Multiple unit root tests are available in the research software, including Dickey–Fuller, Phillips–Perron, Kwiatkowski–Phillips–Schmidt–Shin and Elliott, Rothenberg and Stock Point-Optimal tests.
Estimation[edit]
EViews helps researchers and professionals to estimate linear equations and systems of linear equations models of time series and panel data with various methods. Eviews allows the user to assess econometric results easily.[5]
See also[edit]
References[edit]
- ^Doti, James L.; Adibi, Esmael (1987). Econometric Analysis with MicroTSP Student Software: An Applications Approach. Englewood Cliffs, NJ: Prentice Hall. ISBN0-13-224114-5.
- ^'EViews 12 Enterprise Edition'. Retrieved June 17, 2016.
- ^'Macrobond product overview'. Retrieved June 17, 2016.
- ^'Haver Analytics Third Party Software Review'. Retrieved January 2, 2018.
- ^'Memento on EViews Output'(PDF). Retrieved June 17, 2016.
Further reading[edit]
- Agung, I. Gusti Ngurah (2011). Time Series Data Analysis Using EViews. John Wiley & Sons. ISBN978-1-118-17630-6.
- Griffiths, William E.; Hill, R. Carter; Lim, Guay C. (2011). Using EViews for Principles of Econometrics (Fourth ed.). John Wiley & Sons. ISBN978-1-118-03207-7.
- Vogelvang, Ben (2005). Econometrics: Theory and Applications with EViews. Pearson Education. ISBN0-273-68374-8.
External links[edit]
Simple DummiesThe 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)
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.
Code: Select all
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:
Eviewstub
Would regress Y on a constant, X and SEX=M, and not SEX=F.Eviews Student Version
Fixed Effect DummiesFixed 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)
in your specification to include cross-section fixed effects. For period fixed effects, simply include:
Eviews 11
Eviews For Mac
Remember to be careful of the dummy variable trap. You might want to drop one of the dummies, as outlined above.