1. Ch6 Dummy Variable Regression Models
---The Dummy Variable Technique

2. 1. Basics
1. Quantitative variables vs. Qualitative variables
2.Dummy variables: variables that assume such 0 and 1 values are called dummy variables.

3. Structural or Parameter Stability of regression Models
When we use a regression model involving time series data, it may happen that there is a structural change in the relationship between the regressand and the regrssors.
By structural change, we mean that the values of the parameters of the model do not retain the same through the entire time period.

4. 2. Testing for Structural Stability
How do we find out that a structural change has in fact occurred?
1). The Chow Test
2). The Dummy Variable Technique

5. 2. The weakness of the Chow test
1. The mechanics of the Chow test
2. The weakness of the Chow test

6. 3. The Chow Test
The Chow test shows that under the null hypothesis the regressions (the restricted model and unrestricted model) are statistically the same (i.e., no structural change or break) and the F ratio given above follows the F distribution with k and (n1+n2-2k) df in the numerator and denominator, respectively.
But the Chow test does not tell us whether the difference in the two regressions is because of differences in the intercept terms or the slop coefficients or both.

7. 4.The Dummy Variable Alternative to the Chow Test
is the differential intercept and is the differential slope coefficient ( also called the slope drifter)
The use of dummy variable D enables us to differentiate between the intercepts and between slope coefficients of the two periods