1. "Regression analysis can be used to prove conclusively whether a policy measure that produces an increase in the magnitude of an explanatory variable will cause an increase (or a decrease) in the magnitude of the dependent variable." True, False, Uncertain? Explain.
2. Explain how "endogeneity bias" can distort the implications of a naive regression intended to reveal the effect of participation in a job training program on subsequent earnings.
3. Explain what criteria you might use to choose between a linear specification and a log-linear specification?
4. Suppose you are looking at the output from a probit model to explain whether or not a household purchases a new television in a particular month. The researcher has used a television price index, income of the household, average price of a general movie admission, average price of VCR's and average price of rental videos as explanatory variables. How do you interpret the estimated coefficient on income, and how do you test its statistical significance?
5. Distinguish between "static" and "dynamic" econometric models. In considering a dynamic model that employs a "Koyck geometric lag" structure, what tradeoff must the researcher make?

THE FOLLOWING QUESTIONS REFER TO EXHIBIT A: Income as a function of age, experience and gender

1. How many women are in the sample? _______ What is the minimum years of experience in the sample? _______ What is the correlation between age and experience in the sample? ______ How can the variance in age be 80 when the maximum age in the sample is 49?
2. According to Regressions A1 and A2, age and experience each have strongly significant positive effects on income. The results of Regression A3 conflict with these results. Why?
3. What does Regression A4 imply about average male and female incomes in this sample?
4. What does Regression A5 imply about the difference between average male and female incomes? Does this coincide with the implications from Regression A4? Why or why not? Compared to Regression A3, what has happened to the age effect on income? Why?
5. Regression A6 introduces a number of additional regressors into the income model. Taking into consideration the results from Regression A6 through A10,
   - i.) does this model suffer from any of the following problems? (explain how you made your diagnosis)
   - ii.) if present, what are the consequences of this problem for interpretation of the results of Regression A6?
   a.) multicollinearity?
   b.) heteroskedasticity?
   c.) serially correlated errors?
6. In Regression A6, does someone's age (on average) affect their earnings? If so, how?
7. In Regression A6, does work experience affect incomes? Is this a reliable conclusion, based upon the characteristics of this data set?
8. In regression A6, does gender influence income? Explain.
9. Regression A11 uses the variable WTAGE as the weighting variable in a weighted least squares regression. Explain why this weighting variable was chosen.
10. According to the weighted least squares regression in Regression A11, does additional experience have a statistically significant effect on income if you are a male? If you are female? Explain.
11. Regression A12 is a probit regression, estimated by maximum likelihood. Comment on this choice of specification. What can the model be used to describe? What can the model be used to simulate?

THE FOLLOWING QUESTIONS REFER TO EXHIBIT B: Household ownership of fish as pets. Quarterly data over a 25 year period between 1968:1 and 1992:3.

1. Based upon the output from Regressions B1 and B2, which of the usual maintained hypotheses for OLS regression appears to be violated most seriously? Explain how you made your diagnosis. What are the implications of this diagnosis for the interpretation of Regression B1?
2. What is Regression B3 designed to reveal, and what do the results of Regression B3 imply for this application?
3. How does the procedure invoked by the AUTO command differ from the procedure invoked by the OLS command, and how do the results from Regression B1 differ from those of Regression B4, and why?
4. What is Regression B5 designed to assess? Does the addition of lagged values of the explanatory variables make a significant contribution to the explanatory power of the model?
5. In light of the STAT / pcor output prior to Regression B5, what might account for the individually statistically significant coefficient on the PRICE1 variable in Regression B5?

GENERAL QUESTIONS:

1. In point form, list and very briefly describe five problems that can plague the collection and use of household survey data for econometric analysis. (Think about the discussion during the classroom survey this quarter.)
2. Suppose you are interested in formulating a regression model of the daily demand for white-water rafting trips (TRIPS) to a particular river as a function of the rate of flow of water down that river (RATE), the temperature (TEMP), the rainfall (RAIN), whether it is a weekend (WKND), and whether it is a "summer" day between the Fourth of July and Labor Day, (SUMR). You know that rafting is unpleasant when the water flow is too high (because the water is too deep for rapids to form) or when the water flow is too low (because the water is too shallow to navigate easily). Write SHAZAM code to estimate an appropriate model and also to solve for the RATE that can be expected to result in the largest number of trips.
3. What aspect of Economics 143 most needs improvement? Explain. (Try to respond honestly; full points for any plausible suggestions.)

BONUS QUESTION: