4. Suppose you have quarterly data on new housing starts, interest rates, and real per capita income. Specify a model for housing starts that accounts for possible trends and seasonality in the variables. 5. Consider the model estimated in Example 10.2 in Wooldridge's notes (Equation (10.15) on rst page of the second Wooldridge handout on blackboard). Use the data in INTDEF.dta. a. Find the correlation between the in f and dc f over this sample period and com- ment. b. Add a single lag of in f and de f to the equation and report the results in the usual form. c. Compare the estimated LRP for the effect of ination with that in equation (10.15). Are they vastly different? (1. Are each of the two lags in the model signicant at the 5% level? 6. The le TRAFFICdta contains 108 monthly observations on automobile accidents, traffic laws, and some other variables for California from January 1981 through De- cember 1989. Use this data set to answer the following questions. a. During what month and year did California's seat belt law take effect? When did the highway speed limit increase to 65 miles per hour? (Hint: Use the browse command and look at those dummy variables.) b. Regress the variable log(t0tacc) on a linear time trend and 11 monthly dummy variables, using January as the base month (omitted category). Interpret the coefcient estimate on the time trend. Would you say there is seasonality in total accidents? c. Add to the regression from part (b) the variables wkends, unem, spdlaw, and beltlaw. Discuss the coefficient on the unemployment variable. Does its sign and magnitude make sense to you