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Lecture Undergraduate econometrics, 2nd edition - Chapter 9: Dummy (binary) variables

In this chapter, students will be able to understand: Introduction, the use of intercept dummy variables, slope dummy variables, an example: the university effect on house prices, common applications of dummy variables, testing for the existence of qualitative effects, testing the equivalence of two regressions using dummy variables. | Chapter 9 Dummy Binary Variables 9.1 Introduction The multiple regression model yt P1 PzXt2 P3Xt3 . Pk-X k et 9-1-1 The assumptions of the multiple regression model are Assumptions of the Multiple Regression Model MR1. yt P1 P2X2 Px . PvXtv et t 1 . T MR2. E yt P1 P2Xt2 P3 t3 . PvXtK E et 0 MR3. var yt var et Ơ2 Slide 9.1 Undergraduate Econometrics 2nd Edition -Chapter 9 MR4. cov yt yS cov et eS 0 MR5. The values of xtK are not random and are not exact linear functions of the other explanatory variables. MR6. yt A P1 P2Xt2 PsXt3 . PxXtx ơ2 o et N 0 Ơ2 Assumption MR1 defines the statistical model that we assume is appropriate for all T of the observations in our sample. One part of the assertion is that the parameters of the model pK are the same for each and every observation. Recall that pK the change in E yt when xtK is increased by one unit and all other variables are held constant AE yt dE yt Ax . . dx. . tk other variables held constant tk Slide 9.2 Undergraduate Econometrics 2nd Edition -Chapter 9 Assumption 1 implies that for each of the observations t 1 . T the effect of a one unit change in xtK on E yt is exactly the same. If this assumption does not hold and if the parameters are not the same for all the observations then the meaning of the least squares estimates of the parameters in Equation 9.1.1 is not clear. In this chapter we extend the multiple regression model of Chapter 8 to situations in which the regression parameters are different for some of the observations in a sample. We use dummy variables which are explanatory variables that take one of two values usually 0 or 1. These simple variables are a very powerful tool for capturing qualitative characteristics of individuals such as gender race and geographic region of residence. In general we use dummy variables to describe any event that has only two possible outcomes. We explain how to use dummy variable to account for such features in our model. As a second tool for capturing parameter .