Empirical Exercises
1 Using the data set Teaching Ratings described in Empirical Exercises 4.2, carry out the following exercises.
(a) Run a regression of Course_Eval on Beauty. What is the estimated slope?
(b) Run a regression of Course_Eval on Beauty, including some additional variables to control for the type of course and professor characteristics. In particular, include as additional regressors Intro, OneCredit, Female, Minority, and N N English. What is the estimated e§ect of Beauty on Course_Eval? Does the regression in (a) su§er from important omitted variable bias?
(c) Estimate the coe¢ cient on Beauty for the multiple regression model in (b) using the three-step process in Appendix 6.3. in Stock and Watson textbook (the FrischWaugh theorem). Verify that the three-step process yields the same estimated coe¢ cient for Beauty as that obtained in (b).
(d) Professor Smith is a black male with average beauty and is a native English speaker. He teaches a three-credit upper-division course. Predict Professor Smithís course evaluation.
E6.2 Using the data set CollegeDistance described in Empirical Exercises 4.3, carry out the following exercises.
(a) Run a regression of years of completed education (ED) on distance to the nearest college (Dist).What is the estimated slope?
(b) Run a regression of ED on Dist, but include some additional regressors to control for characteristics of the student, the studentís family, and the local labor market.
In particular, include as additional regressors Bytest, Female, Black, Hispanic, Incomehi, Ownhome, DadColl, Cue80, and Stwmf g80. What is the estimated e§ect of Dist on ED?
(c) Is the estimated e§ect of Dist on ED in the regression in (b) substantively di§erent from the regression in (a)? Based on this, does the regression in (a) seem to su§er from important omitted variable bias?
(d) Compare the Öt of the regression in (a) and (b) using the regression standard errors, R2 and R2. Why are the R2 and R2 so similar in regression (b)?
(e) The value of the coe¢ cient on DadColl is positive. What does this coe¢ cient measure?
(f) Explain why Cue80 and Swmf g80 appear in the regression. Are the signs of their estimated coe¢ cients (+ or -) what you would have believed? Interpret the magnitudes of these coe¢ cients.
(g) Bob is a black male. His high school was 20 miles from the nearest college. His base-year composite test score (Bytest) was 58. His family income in 1980 was $26,000, and his family owned a home. His mother attended college, but his father did not. The unemployment rate in his county was 7.5%, and the state average manufacturing hourly wage was $9.75. Predict Bobís years of completed schooling using the regression in (b).
(h) Jim has the same characteristics as Bob except that his high school was 40 miles from the nearest college. Predict Jimís years of completed schooling using the regression in (b).
E6.3 Using the data set Growth_new described in Empirical Exercise 4.4, but excluding the data for Malta, carry out the following exercises.
(a) Construct a table that shows the sample mean, standard deviation, and minimum and maximum values for the series Growth, TradeShare, YearsSchool, Oil, Rev_Coups, Assassinations, RGDP 60. Include the appropriate units for all entries.
(b) Run a regression of Growth on T radeShare, YearsSchool, Rev_Coups, Assassinations and RGDP 60. What is the value of the coe¢ cient on Rev_Coups? Interpret the value of this coe¢ cient. Is it large or small in a real-world sense?
(c) Use the regression to predict the average annual growth rate for a country that has average values for all regressors.
(d) Repeat (c) but now assume that the country ís value for TradeShare is one standard deviation above the mean.
(e) Why is Oil omitted from the regression? What would happen if it were include
