chapter10

chapter10 - CHAPTER 10 DUMMY VARIABLE REGRESSION MODELS...

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

View Full Document Right Arrow Icon
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
Background image of page 1

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

View Full DocumentRight Arrow Icon
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.
Background image of page 2
Image of page 3
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 Right Arrow Icon
Ask a homework question - tutors are online