The assignment will be handed out in lab this week (1/23), and will be due in lab next week (1/30):

1. Problem 7.1 in the text.

2. Problem 7.2 in the text.

[Note: pdf of problems 7.1 and 7.2]

3. What are the consequences of heteroskedasticity, i.e. if you estimate a hetroeskedastic model with OLS, what problems will you encounter?

4. Using this data set, repeat the example from class, i.e. first regress the
log of salary on a constant and the two variables proxying for experience, years
and years squared (years^{2}):

log(salary) = β_{0} + β_{1}*years + β_{2}*years^{2}
+ u_{t}

Then, form either the residual squared
(resid^{2}), the absolute value of the residual (|resid|), or the log of
the residual squared (log(resid^{2})) as appropriate and perform the
three LM
tests for heteroskedasticity (note: resid is the estimated value of u_{t}).

5. Using the second model of heteroskedasticity, i.e. that |resid| =
α_{0} + α_{1}*years + α_{2}*years^{2},
correct the salary model in problem 4 for hetroskedasticity and reestimate.

6. Test the salary model in problem 4 for heteroskedasticty using White's test. Correct the standard errors using White's correction. How do the coefficients and corrected standard errors compare to those obtained in problem 5?

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