The quantitative independent variables used in regression equations, which usually take values over some continuous range. Frequently, one may wish to include the quality independent variables, often called dummy variables, in the regression model in order to (i) capture the presence or absence of a ‘quality’, such as male or female, poor or rich, urban or rural areas, college degree or do not college degree, different stages of development, different period of time; (ii) to capture the interaction between them; and, (iii) or to take on one or more distinct values. . | Applied Econometrics 1 Dummy Variables Applied Econometrics Lecture 4 Use of Dummy Variables Pure and complete sorrow is as impossible as pure and complete joy 1 Introduction The quantitative independent variables used in regression equations which usually take values over some continuous range. Frequently one may wish to include the quality independent variables often called dummy variables in the regression model in order to i capture the presence or absence of a quality such as male or female poor or rich urban or rural areas college degree or do not college degree different stages of development different period of time ii to capture the interaction between them and iii or to take on one or more distinct values. 2 Intercept Dummy An intercept dummy is a variable says D has the value of either 0 or 1. It is normally used as a regressor in the model. For example the consumption function C can be written as follows C bo biY b2D where Y is the gross national income D is equal to 1 for developing countries and 0 for developed countries Then If D 0 C bo biY If D 1 C bo biY b2D bo b2 biY Illustrative example 1 Maddala 308 We suppose that we regress the consumption C on income Y for household. We include the following quality variables in the form of dummy variables Written by Nguyen Hoang Bao May 22 2004 Applied Econometrics 2 Dummy Variables 1 D1 i 0 if gender is male if gender is female 11 D2 i 0 if age 25 otherwise I1 D3 i 0 if 25 age 50 otherwise I1 D4 i 0 if education high school degree otherwise I1 D5 i 0 if high school degree education college degree otherwise Then we run the following regression equation C a PY Y1D1 Y2 D2 Y3 D3 Y4 D4 Y5 D5 The assumption made in the dummy variable method is that it is only the intercept that changes for each group but not the slope coefficient of Y. Illustrative example 2 Maddala 309 The dummy variable method is also used if one has to take care of seasonal factors. For example if we have quarterly data on C and Y we fit the .
