The McGraw-Hill Series Economics essentials of economics brue, McConnell, and Flynn Essentials of Economics

TABLE 9.9
U.S. Presidential
Elections, 1916–2004
guj75772_ch09.qxd 13/08/2008 05:15 PM Page 312

---

Chapter 9
Dummy Variable Regression Models
313
c.
Chatterjee et al. suggested considering the following model as a trial model to pre-
dict presidential elections:
V
=
β
0
+
β
1
I
+
β
2
D
+
β
3
W
+
β
4
(
G I
)
+
β
5
P
+
β
6
N
+
u
Estimate this model and comment on the results in relation to the results of the model
you have chosen.
9.25. Refer to regression (9.6.4). Test the hypothesis that the rate of increase of average
hourly earnings with respect to education differs by gender and race. (
Hint:
Use mul-
tiplicative dummies.)
9.26. Refer to the regression (9.3.1). How would you modify the model to find out if there
is any interaction between the gender and the region of residence dummies? Present
the results based on this model and compare them with those given in Eq. (9.3.1).
9.27. In the model
Y
i
=
β
1
+
β
2
D
i
+
u
i
, let
D
i
=
0 for the first 40 observations and
D
i
=
1
for the remaining 60 observations. You are told that
u
i
has zero mean and a variance of
100. What are the mean values and variances of the two sets of observations?
*
9.28. Refer to the U.S. savings–income regression discussed in the chapter. As an
alternative to Eq. (9.5.1), consider the following model:
ln
Y
t
=
β
1
+
β
2
D
t
+
β
3
X
t
+
β
4
(
D
t
X
t
)
+
u
t
where 
Y
is savings and 
X
is income.
a.
Estimate the preceding model and compare the results with those given in
Eq. (9.5.4). Which is a better model?
b.
How would you interpret the dummy coefficient in this model?
c.
As we will see in the chapter on heteroscedasticity, very often a log transforma-
tion of the dependent variable reduces heteroscedasticity in the data. See if this
is the case in the present example by running the regression of log of 
Y
on 
X
for
the two periods and see if the estimated error variances in the two periods are sta-
tistically the same. If they are, the Chow test can be used to pool the data in the
manner indicated in the chapter.
9.29. Refer to the Indian wage earners example (Section 9.12) and the data in Table 9.7.
†
As a reminder, the variables are defined as follows:
WI 
=
weekly wage income in rupees
Age 
=
age in years
D
sex
=
1 for male workers and 0 for female workers
DE
2
=
a dummy variable taking a value of 1 for workers with up to a primary
education
DE
3
=
a dummy variable taking a value of 1 for workers with up to a secondary
education
DE
4
=
a dummy variable taking a value of 1 for workers with higher education
DPT
=
a dummy variable taking a value of 1 for workers with permanent jobs and a
value of 0 for temporary workers
The reference category is male workers with no primary education and temporary jobs.
*
This example is adapted from Peter Kennedy, 
A Guide to Econometrics,
4th ed., MIT Press,
Cambridge, Mass., 1998, p. 347.
†
The data come from 
Econometrics and Data Analysis for Developing Countries,
by Chandan 
Mukherjee, Howard White, and Marc Wuyts, Routledge Press, London, 1998, in the Appendix.
guj75772_ch09.qxd 13/08/2008 05:15 PM Page 313

---

314