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This question is adapted from E8.1, of Stock and Watson.
This question is adapted from E8.1, of Stock and Watson. Some sentences in the assignment have been copied verbatim from Introduction to Econometrics 3rd Edition, by Stock and Watson.
This assignment uses a Current Population Survey (CPS) dataset. A similar dataset was used in the Chapter 8 introduction: http://home.cc.umanitoba.ca/~godwinrt/3040/overheads/ch8intro.pdf
A description of the data may be found here:
http://wps.aw.com/wps/media/objects/3833/3925976/datasets2e/datasets/CPS04_Description.pdf
The purpose of this assignment is to give you practise doing an econometric study. The ultimate goal is to determine the effect of obtaining a bachelor's degree (BA) on average hourly earnings, by specifying a suitable regression model.
You can download the data from the course website using the commands:
cps = read.csv("http://home.cc.umanitoba.ca/~godwinrt/3040/assign3/cps04.csv")
attach(cps)
In questions where you are required to run a regression, you should report your results in a table similar to the one found on page 3. A copy of this table is available on the website:
http://home.cc.umanitoba.ca/~godwinrt/3040/assign3/assign3table.docx
or:
http://home.cc.umanitoba.ca/~godwinrt/3040/assign3/assign3table.odt
1. Regress AHE on Bachelor.
a) Interpret the estimated coefficient on Bachelor.
b) Why is it important to add more variables to the model?
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2. Run a regression of average hourly earnings (AHE) on age (Age), gender (Female), and education (Bachelor).
a) Compare the R2 from this regression to the R2 from the regression in question 1. Why has it increased?
b) If Age increases from 25 to 26, how are earnings expected to change?
c) If Age increases from 50 to 51, how are earnings expected to change?
3. Run a regression of the logarithm average hourly earnings, ln(AHE), on Age, Female, and Bachelor.
a) If Age increases from 25 to 26, how are earnings expected to change?
b) If Age increases from 50 to 51, how are earnings expected to change?
4. Run a regression of the logarithm average hourly earnings, ln(AHE), on ln(Age), Female, and Bachelor.
a) What is the estimated effect of Age on AHE in this regression?
b) If Age increases from 25 to 26, how are earnings expected to change?
c) If Age increases from 50 to 51, how are earnings expected to change?
5. Run a regression of the logarithm average hourly earnings, ln(AHE), on Age, Age2, Female, and Bachelor.
a) If Age increases from 25 to 26, how are earnings expected to change?
b) If Age increases from 50 to 51, how are earnings expected to change?
6. The models in questions 3, 4, and 5, are all trying to capture a non-linear effect of Age on AHE. Which model do you think is best, and why?
7. Run a regression of ln(AHE), on Age, Age2, Female, Bachelor and the interaction term Female × Bachelor.
a) What is the estimated effect of an education on earnings, for men and for women?
b) What does the coefficient on the interaction term measure?
c) Do women earn less than men? Does education have a different effect on earnings for women, than it does for men? Use appropriate F-tests.
8. Is the effect of Age on earnings different for males than females? Specify and estimate a regression that you can use to answer this question.
9. Is the effect of Age on earnings different for high school graduates than college graduates? Specify and estimate a regression that you can use to answer this question.
