Economics 3E03: Applied Econometrics
Instructor: Youngki Shin
Fall 2018
Assigned: Oct. 2, 2018
Due: Oct. 15, 2018
Problem Set 2
1. (30) On the course website (A2L), you are asked 10 multiple-choice questions under Assess-
ments >> Quizzes. Submit your answer on the Avenue website. Each multiple-choice
question has 3 marks. The quiz section will be closed at 3:30 pm on October 15, 2018.
2. (20) Suppose that a researcher, using data on class size (CS) and average test scores from
100 third-grade classes, estimates the OLS regression
\TestScore = 520:4 5:82 CS:
(a) (5) A classroom has 22 students. What is the regression’s prediction for that classroom’s
average test score?
(b) (5) Last year a classroom had 19 students, and this year it has 23 students. What is the
regression’s prediction for the change in the classroom average test score?
(c) (10) The sample average class size across the 100 classrooms is 21.4. What is the sample
average of the test scores across the 100 classrooms?
(Hint: Review the formulas for the OLS estimators.)
3. (Numerical Exercises, 30) In this question, we test if there exists a wage di erence between
male and female workers. From the course website, download a data set, cps92 12.csv,
and its description le, CPS92 12 Description.pdf. Read the data description le carefully.
Notice that the rst row of the data set denotes the variable names. Use the following code
and save our main variables:
rm(list=ls())
my.dat=read.csv(’cps92_12.csv’)
# Keep observations only in 2012
dat.2012=subset(my.dat, year==2012)
# Separate out male and female wages
wage.m=subset(dat.2012, female==0)$ahe
wage.f=subset(dat.2012, female==1)$ahe
Note that wage.m and wage.f contain male and female wages in 2012, respectively. The
hypothesis testing problem can be written as
H0 : m f = 0
H1 : m f 6= 0;
where m and f denote the mean male wage and mean female wage, respectively.
1
Economics 3E03: Applied Econometrics
Instructor: Youngki Shin
Fall 2018
Assigned: Oct. 2, 2018
Due: Oct. 15, 2018
(a) (10) Using the dataset, estimate m and f.
(b) (10) Calculate the test statistic for the above hypothesis test.
(Hint: read Section 3.4 of the textbook.)
(c) (5) Calculate the p-value of the test statistic in (b).
(d) (5) Can you reject the null hypothesis?
4. (Numerical Exercises, 20) From the course website (A2L), download a data set, wage2.csv,
under Content >Assignments. This is the same data set with wage1.csv, but the le now
contains variable names in the rst row.
(a) (10) Read the data set and save it as my.data and print out the variable names:
my.data=read.csv(‘wage2.csv’)
print(colnames(my.data))
Run a linear regression of wage on education. Report the estimates of the intercept and
the slope coe cients. When you nish your university education (4 more years), how
much would your wage increase?
(b) (10) Run a linear regression of wage on experience. Report the estimates of the intercept
and the slope coe cients. If a worker has 10 years of experience, what is his/her expected
wage? If he/she works 5 more years, how much will the wage increase on average?