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review questions final

# review questions final - Review Questions for Econometrics...

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Review Questions for Econometrics Final Question 1 Saunders (1993) tested the null hypothesis that the New York Stock Exchange (NYSE) is unaffected by the weather on Wall Street (because security markets behave rational). Using daily data from 1962 to 1989, he estimated the following equation: ^ DJ t = b 0 + 0.10 R t-1 + 0.001J t – 0.017 M t + 0.0005 C t N=6,911 (0.01) (0.0006) (0.004) (0.0002) where DJ t = the percentage change in the Dow Jones industrial average on day t b 0 = an unspecified constant term R t = the daily index capital gain or loss for day t J t = 1 if day t is in January, 0 otherwise M t = 1 if day t is a Monday, 0 otherwise C t = 1 if cloud cover was no more than 20%, -1 if cloud cover was 100%, 0 otherwise a. What would happen if you added a dummy variable equal to 1 if day t was a Tuesday, Wednesday, Thursday, or Friday. Hint: the NYSE is closed on weekends. b. Interpret the coefficient estimate on R and the estimate on M. c. Is C a dummy variable? The author could have chosen to include instead of C, the percentage of cloud cover. How do these two model specifications differ? d. State an alternative hypothesis and test Saunders’ null hypothesis at the 5% significance level. Question 2 You have data on the following variables for all colleges and universities in the U.S. in 1999. Suppose you run a linear regression of number of economics majors on a constant, number of undergraduates in attendance, number of required courses in that school's economics major, average GPA in the major, and number of economics faculty appointments. You are trying to estimate the effect of tough grading on size of major. a. Explain why one might think heteroskedasticity is a problem in this regression. b. Make an assumption about the form of the heteroskedasticity and define your new estimator (be specific).

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