* R. Carter Hill, William E. Griffiths and Guay C. Lim, * Principles of Econometrics, Fourth Edition, Wiley, 2011. * Chapter 15.7 Sets of Regression Equations READ (grunfeld2.dat) / names * The firm identifiers are: * i = 1 General Electric; i = 2 Westinghouse * Pooled least squares - Table 15.11, page 564. OLS inv v k * The DIAGNOS / CHOWONE= command calculates and reports the * Goldfeld-Quandt test statistic as reported above and the * Chow test statistic for the same coefficients for the two firms. * The Chow test is reported in Equation (15.45) page 565. * The Goldfeld-Quandt test is reported on pages 565-6. DIAGNOS / CHOWONE=20 * Generate a dummy variable = 1 for Westinghouse GENR D=(I.EQ.2) * Generate interaction variables GENR DV=D*V GENR DK=D*K * Estimate a dummy variable model - Table 15.12, page 565. OLS INV D V DV K DK * Chow test for the same investment functions for the two firms. * This is the same test reported above following estimaton of * the pooled model. TEST TEST D=0 TEST DV=0 TEST DK=0 END * Separate least squares regressions - Table 15.13, page 566 SET NOWARNSKIP * General Electric SKIPIF (I.NE.1) OLS inv v k GEN1 S2GE=\$SIG2 GEN1 DF1=\$DF DELETE SKIP\$ * Westinghouse SKIPIF (I.NE.2) OLS inv v k GEN1 S2WE=\$SIG2 GEN1 DF2=\$DF DELETE SKIP\$ * Goldfeld-Quandt test for heteroskedasticity GEN1 F=S2GE/S2WE * Get a p-value DISTRIB F / TYPE=F DF1=DF1 DF2=DF2 GEN1 pvalue=1-\$CDF PRINT F pvalue * The pvalue is less than 0.005 to give evidence to reject * the null hypothesis of homoskedastic errors at any * usual significance level. STOP