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ECON7020x By H. Morita Econometrics - Final Exam - Due: 11:59pm, August 25, Sunday, 2013 Part I Collecting data from 250 individuals, you estimate the following relationship: Dependent Variable:...

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ECON7020x By H. Morita
Econometrics - Final Exam - Due: 11:59pm, August 25, Sunday, 2013
Part I
Collecting data from 250 individuals, you estimate the following relationship:
Dependent Variable: In(incorne) Regressors XXXXXXXXXXEduc XXXXXXXXXX XXXXXXXXXX012) Exper XXXXXXXXXX XXXXXXXXXXExper XXXXXXXXXX XXXXXXXXXXMarried XXXXXXXXXXConstant XXXXXXXXXX XXXXXXXXXXSummary Statistics
SSR XXXXXXXXXXR XXXXXXXXXXFe XXXXXXXXXX
where Income is average hourly earnings in dollars, Educ is years of education, Exper is years of work expe-rience, and Married is binary variable that takes 1 if a person is married, and 0 otherwise. The numbers in parenthesis are homoskedasticity-only standard errors.
(a) In terms of goodness of fit, which regression is the best? Explain. (b) In Regression (1), what is the change in Income when Educ increases by 1 year? (c) Using regression (3), find a predicted value of the log of income In (Incomei), for a married worker whose years in education is 15 years, and whose years in work experience is 5 years. (d) In regression (2), what is the (partial) effect on In (Incomei) when Exper increases from 5.5 to 6 years? (e) In Regression (2), are Exper and Exper2 jointly statistically significant at the 1% level?
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Answered Same Day Dec 23, 2021

Solution

David answered on Dec 23 2021
121 Votes
Part 1:
(a) In terms of goodness of fit, which regression is the best? Explain.
The R
2
of a model can be considered as a measure of goodness of fit of that model. In terms of
R
2
, model 3 is the best regression model with highest value among the three models.
(b) In regression (1), what is the change in Income when Educ increases by 1 year?
The regression model 1 is given by
ln(Income) = 0.84 + 0.083 Educ
If the Educ increases 1 year, ln of income increases by 0.083 dollars. Therefore, income
increases by (0.083*100) = 8.3 percent.
(c) Using regression (3), find a predicted value of the log of income Ì‚ , for a ma
ied
worker whose years in education is 15 years, and whose years in work experience is 5 years.
The regression model is given by
ln(Income) = 0.84 + 0.083 Educ + 0.033 Exper – 0.0005 Expe
2
+ 0.062 Ma
ied
Therefore, Ì‚ , for a ma
ied worker whose years in education is 15 years, and whose
years in work experience is 5 years is ,
̂ = 0.84 + 0.083  15 + 0.033  5 – 0.0005  5
2
+ 0.062 ï‚´ 1 = 2.299
(d) In regression (2), what is the (partial) effect on ln(Income) when Exper increases from 5.5 to
6 years?
The regression model is given by
ln(Income) = 0.84 + 0.083 Educ + 0.033 Exper – 0.0005 Expe
2

Partial effect on ln(Income) = 0.84 + 0.083(Educ) + 0.033(6) – 0.0005(6
2
) – (0.84 + 0.083(Educ)
+ 0.033(5.5) - 0.0005(5.5
2
) = 0.0165 – 0.0028 = 0.0137
(e) In Regression (2), are...
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