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dougherty3e_ch05

# dougherty3e_ch05 - Dougherty Introduction to Econometrics...

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Unformatted text preview: Dougherty: Introduction to Econometrics 3e Study Guide Chapter 5 Dummy variables Overview This chapter explains the definition and use of a dummy variable, a device for allowing qualitative characteristics to be introduced into the regression specification. Although the intercept dummy may appear artificial and strange at first sight, and the slope dummy even more so, you will become comfortable with the use of dummy variables very quickly. The key is to keep in mind the graphical representation of the regression model. Learning outcomes After working through the corresponding chapter in the text, studying the corresponding slideshows, and doing the starred exercises in the text and the additional exercises in this guide, you should be able to explain: how the intercept and slope dummy variables are defined • • • • • • what impact they have on the regression specification how the choice of reference (omitted) category affects the interpretation of t tests on the coefficients of dummy variables how a change of reference category would affect the regression results how to perform a Chow test when and why a Chow test is equivalent to a particular F test of the joint explanatory power of a set of dummy variables. Additional exercises A5.1 In Exercise A1.5 the logarithm of earnings was regressed on height using EAEF Data Set 21 and, somewhat surprisingly, it was found that height had a highly significant positive effect. We have seen that the logarithm of earnings is more satisfactory than earnings as the dependent variable in a wage equation. Fitting the semilogarithmic specification, we obtain . reg LGEARN HEIGHT Source | SS df MS Number of obs = 540 -------------+------------------------------ F( 1, 538) = 51.03 Model | 16.1740421 1 16.1740421 Prob &amp;gt; F = 0.0000 Residual | 170.533601 538 .316976954 R-squared = 0.0866 -------------+------------------------------ Adj R-squared = 0.0849 Total | 186.707643 539 .34639637 Root MSE = .56301 ------------------------------------------------------------------------------ LGEARN | Coef. Std. Err. t P&amp;gt;|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- HEIGHT | .0408707 .0057216 7.14 0.000 .0296313 .0521101 _cons | .0261041 .3879608 0.07 0.946 -.7359995 .7882078 ------------------------------------------------------------------------------ The t statistic for HEIGHT is even higher. In Exercise A1.5 it was hypothesized that the effect might be attributable to males tending to have greater earnings than females and also tending to be taller. The © Christopher Dougherty, 2007 The material in this book has been adapted and developed from material originally produced for the degrees and diplomas by distance learning offered by the University of London External System ( www.londonexternal.ac.uk ) Dougherty: Introduction to Econometrics 3e Study Guide output below shows the result of adding the dummy variable to the specification, to control for sex....
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dougherty3e_ch05 - Dougherty Introduction to Econometrics...

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