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Please use the data attached. The data set includes information on wages, education, parents’
education, and several other variables for 1,230 working men in 1991.
You have the following variables given:
wage
hourly wage, 1991
abil
ability measure, not standardized
educ
highest grade completed by 1991
exper
potential experience
motheduc
highest grade, mother
fatheduc
highest grade, father
1. Estimate the regression model
𝑒𝑑𝑢𝑐 = 𝛽0 + 𝛽1 𝑚𝑜𝑡ℎ𝑒𝑑𝑢𝑐 + 𝛽2 𝑓𝑎𝑡ℎ𝑒𝑑𝑢𝑐 + 𝑢
using OLS and report the results in the usual form. How much sample variation in educ is
explained by parents’ education? Interpret the coefficient on motheduc.
2. Add the variable abil (a measure of cognitive ability) to the regression, and report the
results in equation form. Does “ability” help to explain variations in education, even after
controlling for parents’ education? Explain.
3. Please find the model that explains the dependent variable (educ) best. How do you
identify your model?
a) Please interpret the marginal effect associated with the exogenous variables in your
baseline model (model identified in part a)
b) Analyze the residual plots of your baseline model. Are there any indications of
heteroscedasticity in your residuals? Please test and report your results.
c) Analyze the residual plots of your baseline model. Are there any indications of
heteroscedasticity in your residuals? Please test and report your results.
d) Re-estimate your baseline model using heteroscedasticity and autocorrelation robust
errors. Do your coefficients and standard errors change? Why or why not? Please
interpret the marginal effects for each statistically significant coefficient.
e) Please describe briefly how an estimation of a linear relationship between 2 variables
is affected if you ignore a heteroscedasticity problem in an estimated regression
model. (about 3 sentences)
wage
abil
educ
12.01923 5.027738
8.912656 2.03717
15.51433 2.475895
13.33333 3.60924
11.07011 2.636546
17.48252 3.474334
21.09705 -1.76481
11.6671
-0.1336
16.96113 2.072431
11.53846 -0.33846
14.81481 1.38071
20.69917 3.412799
28.68369 4.365496
12.5 1.090446
35.87115 5.082784
23.40322 2.129054
16.64145 3.469769
16.0451 4.268983
12.01923 2.417693
8.534851 3.125001
16.15385 4.236644
14.4609 5.316357
19.77612 2.63304
13.99491 3.046665
17.92453 2.57083
11.05769 1.112235
7.960199 1.088529
12.73585 3.54264
12.23092
-0.0317
19.74026 1.890898
23.68421 4.802668
17.82178 3.646205
11.34454 3.609689
13.1291 3.786006
15.69966 1.759616
7.591093 2.10501
11.38747 1.548639
14.95163 2.905734
22.43437 2.492563
15.57789 2.936283
24.80916 4.194712
13.12649 5.898935
13.91304 2.87161
12 -0.78823
60.89457 3.286462
12.8626 3.255798
10.52632 -1.92598
12.84797 2.321783
16.96676 2.813435
15.02732 4.559115
67.07992 2.757675
exper
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8.413462
4.864636
5.764967
4.637437
18.26923
11.48148
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11.07438
6.134969
7.698888
9.163803
16.34615
10.17699
12.32394
27.45098
14.42308
4.807693
14.95726
17.79514
18.69159
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9.615385
9.134615
8.576923
7.804878
21.22642
19.71154
13.97098
54.60994
13.18102
8.774495
15.86539
52.1327
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5.812937
12.5182
14.73297
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16.72241
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9.202424
15.10791
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13.46154
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12.31127
7.075472
4.573171
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2.151329
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