第七章_联立方程模型和两阶段最小二乘法

第七章联立方程模型和两阶段最小二乘法35404550556065702022242628303234363840CC48121620242022242628303234363840PP253035404550552022242628303234363840WP-8-6-4-202462022242628303234363840II建立一个OBJECT。确定内外生变量：cc=c(1)+c(2)*PP+c(3)*PP(-1)+c(4)*(WP+WG)ii=c(5)+c(6)*PP+c(7)*PP(-1)+c(8)*KKWP=c(9)+c(10)*XX+c(11)*XX(-1)+c(12)*AAINSTWGGGTTAAPP(-1)KKXX(-1)C回归结果：System:KLEINMODELEstimationMethod:Two-StageLeastSquaresDate:07/13/11Time:15:29Sample:19211941Includedobservations:21Totalsystem(balanced)observations63CoefficientStd.Errort-StatisticProb.C(1)16.554761.46797911.277250.0000C(2)0.0173020.1312050.1318720.8956C(3)0.2162340.1192221.8137140.0756C(4)0.8101830.04473518.110690.0000C(5)20.278218.3832492.4188960.0192C(6)0.1502220.1925340.7802370.4389C(7)0.6159440.1809263.4043980.0013C(8)-0.1577880.040152-3.9297510.0003C(9)1.5002971.2756861.1760700.2450C(10)0.4388590.03960311.081550.0000C(11)0.1466740.0431643.3980630.0013C(12)0.1303960.0323884.0260010.0002Determinantresidualcovariance0.287714Equation:CC=C(1)+C(2)*PP+C(3)*PP(-1)+C(4)*(WP+WG)Instruments:WGGGTTAAPP(-1)KKXX(-1)CObservations:21R-squared0.976711Meandependentvar53.99524AdjustedR-squared0.972601S.D.dependentvar6.860866S.E.ofregression1.135659Sumsquaredresid21.92525Prob(F-statistic)1.485072Equation:II=C(5)+C(6)*PP+C(7)*PP(-1)+C(8)*KKInstruments:WGGGTTAAPP(-1)KKXX(-1)CObservations:21R-squared0.884884Meandependentvar1.266667AdjustedR-squared0.864569S.D.dependentvar3.551948S.E.ofregression1.307149Sumsquaredresid29.04686Prob(F-statistic)2.085334Equation:WP=C(9)+C(10)*XX+C(11)*XX(-1)+C(12)*AAInstruments:WGGGTTAAPP(-1)KKXX(-1)CObservations:21R-squared0.987414Meandependentvar36.36190AdjustedR-squared0.985193S.D.dependentvar6.304401S.E.ofregression0.767155Sumsquaredresid10.00496Prob(F-statistic)1.963416联立方程组解得：Model:UntitledDate:07/13/11Time:15:42Sample(adjusted):19211941SolveOptions:Static-StochasticSimulationSolver:BroydenMaxiterations=5000,Convergence=1e-08Requestedrepetitions=1000,Allowupto2percentfailuresSolutiondoesnotaccountforcoefficientuncertaintyinlinkedequationsTrackendogenous:mean,standarddeviationCalculatingInnovationCovarianceMatrixSample:@ALLInsufficientXXinnovations-Equationtreatedasnon-stochasticInsufficientPPinnovations-Equationtreatedasnon-stochasticMatrixscaledtoequationspecifiedvariancesScenario:BaselineSolvebegin15:42:24Repetitions1-200:successful15:42:24Repetitions201-400:successful15:42:24Repetitions401-600:successful15:42:24Repetitions601-800:successful15:42:24Repetitions801-1000:successful15:42:24Solvecomplete15:42:241000successfulrepetitions,0failure(s)1751801851901952002052102152202022242628303234363840KKKK(BaselineMean)-8-6-4-202462022242628303234363840IIII(BaselineMean)1751801851901952002052102152202022242628303234363840KKKK(BaselineMean)两倍标准差下的预测值范围：354045505560657075802022242628303234363840DOWNCCUPCCCC(BaselineMean)