Use the data in MURDER.RAW for this exercise.
(i) Using the years 1990 and 1993, estimate the equation
mrdrteit = (0 + (1d93t + (1execit + (2unemit + ai + uit,t = 1, 2
By pooled OLS and report the results in the usual form. Do not worry that the usual OLS standard errors are inappropriate because of the presence of ai. Do you estimate a deterrent effect of capital punishment?
(ii) Compute the FD estimates (use only the differences from 1990 to 1993; you should have 51 observations in the FD regression). Now what do you conclude about a deterrent effect?
(iii) In the FD regression from part (ii), obtain the residuals, say, i. Run the Breusch-Pagan regression 2i on (execi, (unemi and compute the F test for heteroskedasticity. Do the same for the special case of the White test [that is, regress 2i on i, 2i, where the fitted values are from part (ii)]. What do you conclude about heteroskedasticity in the FD equation?
(iv) Run the same regression from part (ii), but obtain the heteroskedasticity-robust t statistics. What happens?
(v) Which t statistic on (exec. do you feel more comfortable relying on, the usual one or the heteroskedasticity-robust one? Why?