ECO 507 Final exam

  1. Load, inspect, and clean (if necessary) the income data provided on Desire to Learn.
  2. Specify and run an econometric model that explains the variation in yearly earnings
    (from work) among the people in the sample. Present the regression output in a proper table. You must provide a STATA log documenting your complete program. Interactive work is not allowed. The program must run smoothly without errors from start to finish.
  3. Did you create any additional variables from the ones existing? Explain.
  4. Do you think your model suffers from omitted variable bias? Explain (you may consider variables not provided in the dataset. Remember though that if I gave you all the variables I used in data creation – which I did, then there is no omitted variable bias. I guess I’m asking what other variables you would include in a real life, real data project)
  5. Do you think your model suffers from heteroskedasticity? Explain (a formal white test is expected, of course)
  6. Do you think your model suffers from multicollinearity? Explain (a formal vif test is expected, of course)
  7. What other specifications, other than the one you chose, were “close candidates” in your search for the optimal specification? Why did you not choose them?
  8. Why do you think your chosen model has the best specification (given the data)?
  9. Scatter plot the predicted values from your regression versus the residuals. Do you see any patterns that cause you to worry about your model?
  10. Did you transform any of the variables (for example by taking a log, a root, a square etc)? Why or why not?
  11. Did you consider interaction terms? If yes, which? For those who were not included in the final regression, why not?
  12. Discuss your regression results in detail. For each of the following, discuss how they compare to your expectations:
    1. Signs of all coefficients
    2. Magnitudes of all coefficients
    3. R-Squared
    4. Statistical significance of the regression as a whole
  13. Discuss, variable by variable, including variables you included in your model as well as those you chose to omit (if any), what was your decision to omit or include based on.
  14. Does it seem, based on your results, that there is discrimination of some sort in the marketplace? Why or why not?
  15. Explain your choice of the dependent variable.

Appendix – variable description for final dataset

Gender – this variable is equal to one for males, zero for females.

Race – this variable is equal to one for Caucasians, zero for others.

Experience – this variable measures years of experience in the workplace.

Age – this variable measures the current age of the person.

Industry – 1 for a ‘skilled labor’ industry, 0 for unskilled labor. This does not mean that   

            the data has only 2 industries – only that they are categorized.

Schooling – this variable measures the total schooling (in years) of the person.

Total income – this variable measures total income (from all sources) of the person last year. Other income – this variable measures only the non-work income of the person last year.

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