Essentials of Econometrics
Tutorial Exercises SW 4
4.1 Suppose that a researcher, using data on class size (CS) and average test scores from 100
third-grade classes, estimates the OLS regression,
T estScore \ = 520.4 − 5.82 × CS, R2 = 0.08, SER = 11.5
(a) A classroom has 22 students. What is the regression’s prediction for that classroom’s average
test score?
(b) 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) 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.)
(d) What is the sample standard deviation of test scores across the 100 classrooms? (Hint:
Review the formulas for the R2 and SER.)
4.2 Suppose that a random sample of 200 twenty-year-old men is selected from a population and
that these men’s height and weight are recorded. A regression weight on height yields
W eight \ = −99.41 + 3.94 × Height, R2 = 0.81, SER = 10.2,
where W eight is measure in pounds and Height is measured in inches.
(a) What is the regression’s weight prediction for someone who is 70 inches tall? 65 inches tall?
74 inches tall?
(b) A man has a late growth spurt and grows 1.5 inches over the course of a year. What is the
regression’s prediction for the increase in this man’s weight?
(c) Suppose that instead of measuring weight and height in pounds and inches, these variables
are measured in centimeters and kilograms. What are the regression estimates from this new
centimeter-kilogram regression? (Give all results, estimated coefficients, R2
, and SER.)
4.3 A regression of average weekly earnings (AW E, measured in dollars) on age (measured in
years) using a random sample of college-educated full-time workers aged 25-65 yields the
following:
AW E \ = 696.7 + 9.6 × Age, R2 = 0.023, SER = 624.1.
(a) Explain what the coefficient values 696.7 and 9.6 mean.
(b) The standard error of the regression (SER) is 624.1. What are the units of measurement of
SER (dollars? years? or is SER unit-free?)
(c) The regression R2
is 0.023. What are the units of measurement for the R2
? (dollars? years?
or is R2 unit free?)
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(d) What is the regression’s predicted earnings for a 25-year-old worker? A 45-year-old worker?
(e) Will the regression give reliable predictions for a 99-year-old worker? Why or why not?
(f) Given what you know about the distribution of earnings, do you think it is plausible that the
distribution of errors in the regression is normal? (Hint: Do you think that the distribution
is symmetric or skewed? What is the smallest value of earnings, and is it consistent with a
normal distribution?)
(g) The average age in this sample is 41.6 years. What is the average value of AW E in the
sample? (Hint : Review Key Concept 4.2.)
4.5 A professor decides to run an experiment to measure the effect of time pressure on final
exam scores. He gives each of the 400 students in his course the same final exam, but some
students have 90 minutes to complete the exam while others have 120 minutes. Each student
is randomly assigned one of the examination times based on the flip of a coin. Let Yi denote
the number of points scored on the exam by the i
th student (0 ≤ Yi ≤ 100), let Xi denote the
amount of time that the student has to complete the exam (Xi = 90 or 120), and consider
the regression model Yi = βo + β1Xi + ui
.
(a) Explain what the term ui represents. Why will different students have different values of ui?
(b) Explain why E(ui
|Xi) = 0 for this regression model.
(c) Are the other least squares assumptions satisfied (Key Concept 4.3 in Stock and Watson)?
Explain.
(d) The estimated regression is Ybi = 49 + 0.24Xi
(i) Compute the estimated regression’s prediction for the average score of students given 90
minutes to complete the exam. Repeat for 120 minutes and 150 minutes.
(ii) Compute the estimated gain in score for a student who is given an additional 10 minutes
on the exam.
4.6 Show that the first least squares assumption, E(ui
|Xi) = 0, implies that E(Yi
|Xi) = β0 +β1X
4.7 Show that βb0 is an unbiased estimator of β0. (Hint: Use the fact that βb1 is unbiased, which
is shown in Appendix 4.3 of the Stock and Watson textbook).
4.8 Suppose that all of the least squares assumptions (Key Concept 4.3) are satisfied except
that the first assumption E(ui
|Xi) = 0 is replaced by E(ui
|Xi) = 2. Which parts of the Key
Concept 4.4 (Large sample distributions of βb0 and βb1) continue to hold? Which change? Why?
(Is βb1normally distributed in large samples with mean and variance given in Key Concept 4.4?
What about βb0 ?)
4.9
(a) A linear regression yields βb1 = 0. Show that R2 = 0.
(b) A linear regression yields R2 = 0. Does this imply that βb1 = 0?
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4.11 Consider the regression model Yi = β0 + β1Xi + ui
.
(a) Suppose you know that β0 = 0. Derive a formula for the the least squares estimator of β1.
(b) Suppose you know that β0 = 4. Derive a formula for the the least squares estimator of β1.
4.13 Suppose that Yi = β0 + β1Xi + κui
, where κ is a non-zero constant and (Yi
, Xi) satisfy
the three least squares assumptions. Show that the large sample variance of βb1 is given by
σ
2
βb1
= κ
2 1
n
var[(Xi−µX)ui]
[var(Xi)]2
]. [Hint: This equation is the variance given in question 4.10 multiplied
