Empirical Exercises SW6
E6.1 Using the data set TeachingRatings 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 vari-
ables to control for the type of course and professor characteristics. In particu-
lar, include as additional regressors Intro, OneCredit, F emale, M inority, 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 Frisch-
Waugh 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, F emale, 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
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$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 mini-
mum and maximum values for the series Growth, T radeShare, Y earsSchool,
Oil, Rev_Coups, Assassinations, RGDP 60. Include the appropriate units for
all entries.
(b) Run a regression of Growth on T radeShare, Y earsSchool, 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 countryís value for T radeShare is one stan-
dard deviation above the mean.
(e) Why is Oil omitted from the regression? What would happen if it were include
