chapter10

# chapter10 - CHAPTER 10 DUMMY VARIABLE REGRESSION MODELS...

This preview shows pages 1–3. Sign up to view the full content.

CHAPTER 10 DUMMY VARIABLE REGRESSION MODELS QUESTIONS 10.1. ( a ) and ( b ) These are variables that cannot be quantified on a cardinal scale. They usually denote the possession or nonpossession of an attribute, such as nationality, religion, sex, color, etc. ( c ) Regression models in which explanatory variables are qualitative are known as ANOVA models. ( d ) Regression models in which one or more explanatory variables are quantitative, although others may be qualitative, are known as ANCOVA models. ( e ) In a regression model with an intercept, if a qualitative variable has m categories, one must introduce only ( m – 1) dummy variables. If we introduce m dummies in such a model, we fall into the dummy variable trap, that is, we cannot estimate the parameters of such models because of perfect (multi)collinearity. ( f ) They tell whether the average value of the dependent variable varies from group to group. ( g ) If the rate of change of the mean value of the dependent variable varies between categories, the differential slope dummies will point that out. 10.2. ( a ) Quantitative ( b ) qualitative ( c ) quantitative ( d ) qualitative ( e ) quantitative ( f ) qualitative, if expressed in broad categories, but quantitative if expressed as years of schooling ( g ) qualitative ( h ) qualitative ( i ) qualitative ( j ) qualitative. 10.3. ( a ) If there is an intercept term in the model, 11 dummies. ( b ) If there is an intercept term in the model, 5 dummies. 79

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
10.4. ( a ) Here we will fall into the dummy variable trap , because the four columns of the dummy variables will add up to the first column (representing the intercept). ( b ) This equation can be written as: t t t t t t t u M A M A B u M B B M B B B GNP + + + = + - + + + = - - 1 3 2 1 1 4 3 4 2 1 ) ( ) ( where ) ( 4 2 2 B B A + = and ). ( 4 3 3 B B A - = Although we can estimate 1 B , 2 A , and 3 A , we cannot estimate 2 B , 3 B , and 4 B uniquely. The problem here is that the third explanatory variable in the original model, ), ( 1 - - t t M M is a linear combination of t M and 1 - t M , thereby leading to perfect collinearity. 10.5. ( a ) False . Letting D take the values of (0, 2) will halve both the estimated 2 B and its standard error, leaving the t ratio unchanged. ( b ) False . Since the dummy variables do not violate any of the assumptions of OLS, the estimators obtained by OLS are unbiased in small as well as large samples. 10.6. ( a ) Each regression coefficient is expected to be positive. ( b ) 2 B tells us by how much the average salary of a Harvard MBA differs from the base category, which is non-Harvard and non-Wharton MBAs. ( c ) It probably suggests that the Harvard MBA has a premium over the Wharton MBA. 10.7.
This is the end of the preview. Sign up to access the rest of the document.

## This document was uploaded on 10/22/2009.

### Page1 / 12

chapter10 - CHAPTER 10 DUMMY VARIABLE REGRESSION MODELS...

This preview shows document pages 1 - 3. Sign up to view the full document.

View Full Document
Ask a homework question - tutors are online