1
Problem Set 5
Economics 103
Due:
Thursday, March 12, 2009
Question 1
True/False/Explain:
1)
The linear probability model is the application of the multiple regression model with a
continuous lefthand side variable and a binary variable as at least one of the regressors.
2)
The linear probability model has predicted values that lie between 0 and 1.
3)
The probit model is the same as the logit model.
4)
In the expression Pr(Y=1)=
(
β
0
+
β
1
X) from a probit model,
1
cannot be negative since
probabilities have to lie between 0 and 1.
5)
In the probit model
12
0 1
1
2
Pr(
1
,
,...,
)
(
...
)
kx
k
k
YX
XX
X
X
X
, the
slopes tell you the effect of a unit increase in
X
on the probability of
Y
.
6)
In the expression Pr(
deny
= 1
P/I Ratio
,
black
) =
(–2.26
+ 2.74
P/I ratio
+ 0.71
black
),
the effect of increasing the P/I ratio from 0.3 to 0.4 for a white person is 2.74 percentage
points.
7)
In the binary dependent variable model, a predicted value of 0.6 means that the most
likely value the dependent variable will take on is 60 percent.
8)
In the linear probability model, the interpretation of the slope coefficient is the change in
probability that
Y
=1 associated with a unit change in
X
, holding other regressors constant.
9)
The distinction between endogenous and exogenous variables is that exogenous variables
are determined inside the model and endogenous variables are determined outside the
model.
This preview has intentionally blurred sections. Sign up to view the full version.
View Full Document2
Question 2
1.
Describe why you would need to use an instrumental variables estimation strategy?
2.
What makes an instrument valid?
3.
What is the intuition behind IV estimation?
Question 3
Suppose that you are interested in estimating the effect of gender on the probability of college
enrollment, holding constant the person’s family income. To estimate this effect, you estimated a
Logit regression with both household income (INCOME), measured in thousands of dollars, and
an indicator for whether the applicant was female (FEMALE):
)
37
.
1
35
.
0
23
.
4
(
)
,

1
Pr(
FEMALE
INCOME
F
FEMALE
INCOME
enroll
1.
A female student applicant has a household income of 15. What is the probability that she
This is the end of the preview.
Sign up
to
access the rest of the document.
 Winter '07
 SandraBlack
 Economics, Regression Analysis, Web page, Yi, Mexican government, Linear Probability Model

Click to edit the document details