Econ 526/ Manopimoke
Fall2014
Econometrics Practice Final
Topics to Review for the exam: Chapters: 6, 7, 8, 9, 11, 12
The exam will be a TAKE HOME EXAM, which will be posted on the course website at 5 pm. on Thursday
Dec 18. Please type up your answers or scan your written answers and submit it to me at
pymm@ku.edu at 10 pm. on Dec 19. Only PDF files will be accepted. NO LATE SUBMISSIONS OR
ILLEGIBLE EXAMS WILL BE CONSIDERED.
Some practice questions (not comprehensive) are below – more will be covered during the review
session on 12/11. Working on the back of the book questions will also be useful (solutions are already
on the website).
Question 1
Suppose that there are threshold effects when examining the relationship between class size and
student test scores. In particular, when the class size is less than 20, students do well and when the
class size is > 25 students do poorly. How would you test this hypothesis in a linear regression? Suppose
you have data from California that supports your hypothesis. Discuss whether these results would hold
for Kansas in terms of external validity.
Question 2
Refer to Table 8.3 on the following page to answer the following questions.
a. Use specification (5) to determine the change in test scores from having a student teacher ratio of 20
to 30 when students are in a high English Language Learner district and 50 percent are eligible for free
or reduced lunch, and log income is equal to 2.2516.
b. Using the output can you reject the null hypothesis that student-teacher ratio has a linear effect on
test scores?
c. What is the interpretation of the coefficient on log income in Model 5? Is this a large or small effect?
d. What are the threats to internal validity in Model 1?
e. Explain the rationale behind Model 6, why are the interaction terms included in this model. F-test (c)
tests the null hypothesis that the coefficients on the interaction terms are all zero. Explain what this
means in terms of the model.
Question 3
A study investigated the impact of house price appreciation on household mobility. The underlying idea
was that if a house were viewed as one part of the household's portfolio, then changes in the value of
the house, relative to other portfolio items, should result in investment decisions altering the current
portfolio. Using 5,162 observations, the logit equation was estimated as shown in the table, where the
dependent variable is one if the household moved in 1978 and is zero if the household did not move:
Regression
model
constant
Male
Black
Married78
marriage
change
A7983
PURN
Pseudo-R2
Logit
-3.323
(0.180)
-0.567
(0.421)
-0.954
(0.515)
0.054
(0.412)
0.764
(0.416)
-0257
(0.921)
-4.545
(3.354)
0.016
where male, black, married78, and marriage change are binary variables. They indicate, respectively, if
the entity was a male-headed household, a black household, was married, and whether a change in
marital status occurred between 1977 and 1978. A7983 is the appreciation rate for each house from
1979 to 1983 minus the SMSA-wide rate of appreciation for the same time period, and PNRN is a
predicted appreciation rate for the unit minus the national average rate.
(a) Interpret the results. Comment on the statistical significance of the coefficients. Do the slope
coefficients lend themselves to easy interpretation?
(b) The mean values for the regressors are as shown in the accompanying table.
Variable
male
black
married78
marriage change
A7983
PNRN
Mean
0.82
0.09
0.78
0.03
0.003
0.007
Taking the coefficients at face value and using the sample means, calculate the probability of a
household moving.
(c)
Given this probability, what would be the effect of a decrease in the predicted appreciation rate
of 20 percent, that is A7983 = –0.20?
Question 4
Consider the following model of demand and supply of coffee:
Demand:
Supply:
= β1
= β3
+ β2
+ β4
+ ui
+ β5Weather + vi
(variables are measure in deviations from means, so that the constant is omitted).
(a) Suppose you want to estimate the price elasticity of demand by running an OLS regression on the
demand equation. Will your estimate of β1 be unbiased? It not, explain why.
(b) Suppose you have an exogenous variable Weather. Why can this variable be useful in estimating β1?
Outline the steps that you would follow to estimate β1. Will this give you a consistent estimate of
β1? Explain.
Question 5
Do we care more about internal or external validity in cross-sectional estimation? What about time2
series estimation? We care about R in time-series estimation. Is this also what we care about most in
cross-sectional estimation? Explain.
Question 6
Explain why when testing joint hypotheses simultaneously, testing them sequentially ("one at a time"
method) using a series of t-statistics gives unreliable results. What approach should you use instead to
test joint hypotheses? (Hint: Use an example to help explain your answer. Suppose you are testing the
joint hypotheses β1=0 and β1=0. What is the probability of rejecting the joint null hypothesis under using
the “on at a time” method? Is it too low or too high?)