CHAPTER 9: DUMMY VARIABLE REGRESSION MODELS

ü  The Nature of Dummy Variable

Dummy variables – such variables are thus essentially a device to classify data into mutually exclusive categories such as male or female.

ü  Caution in the Use of Dummy Variables

1.      If a qualitative variable has m categories, introduce only (m – 1) dummy variables.

For each qualitative regressor the number of dummy variables introduced must be one less than the categories of that variable.

2.      The category for which no dummy variable is assigned is known as the base, benchmark, control, comparison, reference, or omitted category.

3.      The intercept value (β₁) represents the mean value of the benchmark category.

4.      The coefficients attached to the dummy variables are known as the differential intercept coefficients because they tell how much the value of the intercept that receives the value of 1 differs from the intercept coefficient of the benchmark category.

5.      If a qualitative variable has more than one category, the choice of the benchmark category is strictly up to the researcher.

6.      There is a way to circumvent this trap by introducing as many dummy variables as the number of categories of that variable, provided we do not introduce the intercept in such a model.

7.      Which is a better method of introducing a dummy variable: (1) introduce a dummy for each category and omit the intercept term or (2) include the intercept term and introduce only (m – 1) dummies.

ü  The Dummy Variable Alternative to the Chow Test

Four possibilities:

1.      Both the intercept and the slope coefficients are the same in the two regressions. This, the case of coincident regressions.

2.      Only the intercepts in the two regressions are different but the slopes are the same. This is the case of parallel regressions.

3.      The intercepts in the two regressions are the same, but the slopes are different. This is the situation of concurrent regressions.

4.      Both the intercepts and slopes in the two regressions are different. This is the case of dissimilar regressions.

ü  The Use of Dummy Variables in Seasonal Analysis

Note that to avoid the dummy variable trap, we are assigning a dummy to each quarter of the year, but omitting the intercept term.

ü  Topics for Further Study

Several topics related to dummy variables are discussed in the literature that are rather advanced, including random, or varying, parameters models, switching regression models and disequilibrium models.