Essentials of Econometrics

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Essentials of Econometrics
Empirical Exercises SW4
E4.1 Using the data set CPS08 that contains an extended version of the data set used in
Table 3.1 for 2008 in the Stock and Watson textbook. It contains data for full-time,
full-year workers, age 25-34, with a high school diploma or B.A./B.S. as their highest
degree. A detailed description is given in CPS08_Description, also available on
the website. (These are the same data as in CPS92_08 but are limited to the year
2008.) In this exercise you will investigate the relationship between a workerís age
and earnings. (Generally, older workers have more job experience, leading to higher
productivity and earnings.)
(a) Run a regression of average hourly earnings (AHE) on age (Age). What is the
estimated intercept? What is the estimated slope? Use the estimated regression
to answer this question: How much do earnings increase as workers age by one
year?
(b) Bob is a 26-year-old worker. Predict Bobís earnings using the estimated regression. Alexis is a 30-year-old worker. Predict Alexisís earnings using the
estimated regression
(c) Does age account for a large fraction of the variances in earnings across individuals?
Explain.
E4.2 Using the data set TeachingRatings that contains data on course evaluations, course
characteristics, and professor characteristics for 463 courses at the University of Texas
at Austin. A detailed description is given in TeachingRatings_Description, also
available on the website. One of the characteristics is an index of the professorís
“beauty” as rated by a panel of six judges. In this exercise you will investigate how
course evaluations are related to the professorís beauty.
(a) Construct a scatterplot of average course evaluations (Course_Eval) on the professorís beauty (Beauty): Does there appear to be a relationship between the
variables?
(b) Run a regression of average course evaluations (Course_Eval) on the professorís
beauty (Beauty). What is the estimated intercept? What is the estimated
slope? Explain why the estimated intercept is equal to the sample mean of
Course_Eval: (Hint: what is the sample mean of Beauty?)
(c) Professor Watson has an average value of Beauty, while Professor Stockís value of
Beauty is one standard deviation above the average. Predict Professor Stockís
and Professor Watsonís course evaluations.
(d) Comment on the size of the regressionís slope. Is the estimated e§ect of Beauty
on Course_Eval large or small? Explain what you mean by “large” and “small.”
(e) Does Beauty explain a large fraction of the variance in evaluations across courses?
Explain.
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E4.3 Using the data set CollegeDistance that contains data from a random sample of
high school seniors interviewed in 1980 and re-interviewed in 1986. In this exercise
you will use these data to investigate the relationship between the number of completed years of education for young adults and the distance from each studentís high
school to the nearest four-year college. (Proximity to college lowers the cost of education, so that students who live closer to a four-year college should, on average,
complete more years of higher education.) A detailed description is give in CollegeDistance_Description, also available on the website.
(a) Run a regression of years of completed education (ED) on distance to the nearest
college (Dist); where Dist is measured in tens of miles. (For example, Dist = 2
means that the distance is 20 miles.) What is the estimated intercept? What is
the estimated slope? Use the estimated regression to answer this question: How
does the average value of years of completed schooling change when colleges are
built close to where students go to high school?
(b) Bobís high school was 20 miles from the nearest college. Predict Bobís years of
completed education using the estimated regression. How would the prediction
change if Bob lived 10 miles from the nearest college?
(c) Does distance to college explain a large fraction of the variance in educational
attainment across individuals? Explain.
(d) What is the value of the standard error of the regression? What are the units for
the standard error (meters, grams, years, dollars, cents, or something else)?
E4.3 Using the data set Growth that contains data on average growth rates over 1960-1995
for 65 countries, along with variables that are potentially related to growth. A detailed
description is given in Growth_Description, also available on the website. In this
exercise you will investigate the relationship between growth and trade.
(a) Construct a scatterplot of average annual growth rate (Growth) on the average
trade share (T radeShare). Does there appear to be a relationship between the
variables?
(b) One country, Malta, has a trade share much larger than the other countries. Find
Malta on the scatterplot. Does Malta look like an outlier?
(c) Using all observations, run a regression of Growth on T radeShare. What is the
estimated slope? What is the estimated intercept? Use the regression to predict
the growth rate for a country with trade share of 0.5 and with a trade share equal
to 1.0.
(d) Estimate the same regression excluding the data from Malta. Answer the same
questions in (c).
(e) Where is Malta? Why is the Malta trade share so large? Should Malta be
included or excluded from the analysis?

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