* Reference: Chapter 13 of * Jeffrey M. Wooldridge, Introductory Econometrics: A Modern Approach, * South-Western College Publishing, 2000. SAMPLE 1 53 READ (CRIME3.shd) district year crime clrprc1 clrprc2 d78a avgclr & lcrime clcrime cavgclr cclrprc1 cclrprc2 & dist78 year78 crime78 clrp178 clrp278 d78b avgclr78 & lcrime78 clcri78 cavgc78 cclrp178 cclrp278 * Data validation check IF (year.ne.72) STOP IF (year78.ne.78) STOP SET MISSVALU=-999 SET SKIPMISS SET NOWARNMISS * Example 13.6 * Differencing across the 2 years. GENR dlcrime=log(crime78)-log(crime) GENR dclear1=clrp178-clrprc1 GENR dclear2=clrp278-clrprc2 * Equation (13.22), p. 425. OLS dlcrime dclear1 dclear2 / LOGLIN * For an interpretation of the coefficients when the dependent * variable is log-transformed and the explanatory variable is * in levels see Example 3.2, pp. 74-75. * For an interpretation of the coefficients for a distributed * lag model see Section 10.2, pp. 313-315. * The estimated coefficient on the variable dclear2 is -0.0132. * This suggests that a one unit increase in the clear-up * percentage two years ago (assuming this is a temporary change not * sustained in the next period) leads to a decline in log(crime) by * 0.0132. This measures a proportionate change in the crime rate. * The percentage drop in the crime rate in response to a one unit * increase in the clear-up percentage two years ago is * 100*0.0132 = 1.32 %. * (For a discussion of proportionate changes versus percentage * changes Appendix A.3, pp. 648-650). * Assuming a permanent change in the clear-up percentage * (the percentage of crimes that lead to a conviction), * estimate the long-run percentage change in the crime rate * for a one percentage point increase in the clear-up rate. TEST 100*(dclear1+dclear2) * Now re-scale the dependent variable so that the estimated * coefficients now have an interpretation as percentage changes * rather than proportionate changes. GENR dlcrime=100*dlcrime OLS dlcrime dclear1 dclear2 / LOGLIN TEST dclear1+dclear2 STOP