Greg Smith
11/8/2009
Dr. Cuellar – Econ 317
Problem Set #6
a.
Construct a variable to account for a non-linear relationship between wages and
experience. Write out your new regression model and explain and interpret the
variables.
X4 = X1(^2)
Wage = β0 + β1X1 + β2X2 + β3X3 + β4X4 + U.
b.
i.
What are the expected signs of the coefficients?
Experience = positive
Female = negative
Female Experience = negative
Experience Squared = Negative (diminishing returns)
ii.
Write out the regression equation for men.
Wage = β
0
+ β
1
X
1
+ β
4
X
4
+ U.
iii.
Write out the regression equation for women.
Wage = (β
0
+ β
2
) + (β
1
+ β
3
)X
1
+ β
4
X
4
+ U.
c.
Using the data set Wage1.dta, estimate the above equation:
. reg wage exper female femexp exper2
Source |
SS
df
MS
Number of obs =
526
-------------+------------------------------
F(
4,
521) =
34.31
Model |
1492.95648
4
373.239119
Prob > F
=
0.0000
Residual |
5667.45781
521
10.878038
R-squared
=
0.2085
-------------+------------------------------
Adj R-squared =
0.2024
Total |
7160.41429
525
13.6388844
Root MSE
=
3.2982
------------------------------------------------------------------------------
wage |
Coef.
Std. Err.
t
P>|t|
[95% Conf. Interval]
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0
5
10
15
20
25
0
10
20
30
40
50
years potential experience
average hourly earnings
Fitted values
Fitted values
-------------+----------------------------------------------------------------
exper |
.3101882
.039893
7.78
0.000
.2318173
.3885591
female |
-1.421982
.4618985
-3.08
0.002
-2.329394
-.5145693
femexp |
-.0572396
.0212425
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- Spring '07
- Eyler
- Statistics, Econometrics, Linear Regression, Regression Analysis, Errors and residuals in statistics
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