Problem Set 5
Practice for Prelim 2
Due Tuesday 04/13/10 at the beginning of class
Econ 3200  Introduction to Econometrics
Spring 2010
Cornell University
Prof. Molinari
The exam will consist of TWO parts totaling 100 points. It will be a CLOSED BOOK exam.
However, useful formulae will be provided, and you can ±nd them here on pages 5+. Calculators
Normal,
t
,
2
and
F
of these Tables will be attached to the text of the exam. You will have 75 minutes
to complete
the exam.
Part 1
20 points (4 points each)
In
WORDS
, brie²y
(1 paragraph maximum) describe the following:
1. OLS Estimator.
2. Total Sum of Squares.
3. Multicollinearity.
4. Interpreting the slope coe¢ cient of a loglevel regression.
5. VarianceCovariance Matrix.
1
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View Full DocumentPart 2
80 points (Each subquestion has the number of corresponding points in brackets)
This part of the homework consists of a series of questions related to real empirical appli
cations.
1. We have often talked in class about regression models that are used to explain wages.
In this question we take a closer look at a wage regression. A random sample of 936
individuals was gathered for the following variables:
ln(
wage
)
= natural log of hourly
wage,
educ
= years of education,
exper
= years of experience. Assume for now that
assumptions MLR1MLR5 hold for
ln(
wage
i
) =
&
0
+
1
educ
i
+
2
exper
i
+
u
i
:
(1)
This regression was estimated by OLS yielding the following:
ln(
w
b
age
i
) = 0
:
42
+0
:
072
educ
i
+0
:
020
exper
i
(0
:
0072)
R
2
= 0
:
096
SSR
= 173
:
4
SST
= 192
:
0
(2)
In addition, the following two variable regression models were estimated. Standard errors
are in parentheses:
ln(
w
b
age
i
)
=
0
:
91
+0
:
053
educ
i
R
2
= 0
:
0658
SSR
= 179
:
4
SST
= 192
:
0
(0
:
052)
(0
:
006)
(3)
ln(
w
b
age
i
)
=
1
:
57
+0
:
004
exper
i
R
2
= 0
:
0016
SSR
= 191
:
7
SST
= 192
:
0
(0
:
042)
(0
:
0033)
(4)
e
b
duc
i
=
16
:
11
&
0
:
229
exper
i
R
2
= 0
:
207
SSR
= 3571
:
4
SST
= 4506
:
8
(0
:
181)
(0
:
014)
(5)
(a) Interpret the estimates of
1
and
2
in the regression of
ln(
wage
)
on
educ
and
exper
. Do these estimates make sense? Do the estimates suggest that education and
experience have a big e/ect on wages? Why or why not?
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 Spring '08
 NEILSEN
 Econometrics, Linear Regression, Regression Analysis, Null hypothesis, Yi, regression model assumptions

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