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This article mainly explains "the definition of Eviews virtual variable and intercept change model regression". The explanation in this article is simple and clear and easy to learn and understand. Please follow the editor's train of thought to study and learn "the definition of Eviews virtual variable and intercept change model regression".

Virtual variable (Dummy Variables), also known as fictitious variable, nominal variable or dumb variable, is an artificial variable that reflects the attribute of quality. it is a quantized independent variable, usually with a value of 0 or 1. The introduction of dumb variables can make the linear regression model more complex, but the description of the problem is more concise, one equation can achieve the function of two equations, and close to the reality. This paper first introduces how to define virtual variables and the regression and analysis of virtual variables which only contain intercept changes in the model.

one

data

The data of national wine sales by season from 1982 to 1986 (ten thousand tons) are shown in the table below. Try to establish a seasonal sales model of wine sales volume to time t. The data are as follows:

First draw the time series diagram of y, open yseries, and click view-graph:

Select line to get the sequence diagram as follows:

This is a seasonal time series data, showing obvious seasonal variation characteristics, by adding seasonal virtual variables to describe the seasonal characteristics to establish a model.

Variables and models only contain the regression and analysis of virtual variables with intercept changes.

two

Define virtual variables

For seasonal virtual variables, the definition is as follows:

It is important to note that only three of the four seasons are defined, otherwise it will cause multiple collinearity, because D1+D2+D3+D4 is always equal to 1, which is the "virtual variable trap".

Then generate the virtual variable in Eviews:

Follow the above procedure to define D2 and D3 in turn.

After the virtual variable is defined, the model is established and estimated.

To conduct a return:

The output is as follows:

As can be seen from the output above

As can be seen from the above output results, the t-test value of D2 and D3 is less than the critical value, so the regression parameters of D2 and D3 are not significant, so the virtual variables D2 and D3 are excluded from the model.

Re-estimate the parameters:

The output is as follows:

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