Dummy variable models:

In the linear regression models considered in previous units so far we have assumed the explanatory variables (i.e., the Xs) to be numerical or quantitative in nature. But this may not always be the case. There can be instances when the explanatory variable(s) are qualitative in nature. These qualitative variables are often called the dummy variables. The purpose of this unit is to consider the role of such qualitative explanatory variables in the regression analysis and also to show how the use of dummy variables make the linear regression models an extremely flexible tool for handling many interesting problems encountered in empirical studies.

In many instances in regression equations we have explanatory variables which are qualitative in nature. It is difficult to quantify these qualitative variables as they at best can be divided into certain categories. In such cases we use dummy variable model.

哑变量会影响拦截或slope or both. Accordingly, we take intercept or slope dummies. Remember that dumpy variables are used, as are explanatory variables, on the basis of the logic we build up. Thus behind every regression model there is a theoretical basis.

Dummy variables can be used in seasonal analysis. It also can be used in pooling cross-sectional and time series data.