计量经济学第二次作业异方差检验

1、第三章13题下表列出了中国某年按行业分的全部制造业国有企业及规模以上制造业非国有企业的工业总产值Y,资产合计K及职工人数L。序工业总产值资产合计职工人数L号Y序工业总产值资产合计职工人数L号Y（亿元）K（亿元）（万人）13722.73078.2211321442.521684.436711752.372742.778441451.291973.8227序工业总产资产合计职工人数号值Y（亿元）K（亿元）L（万人）4429.193785.91615749.028688.03254_1781.372798.9831243.071808.4433812.71118.814362291.161758.7

2、7120181899.72052.166171345.17939.158193692.856113.112408656.77694.9431204732.99228.252229370.18363.4816212180.232866.6580101590.362511.9966222539.762545.639611616.71973.7358233046.954787.922212617.94516.0128242192.633255.2916355149.35917.01327解：先对Y二AKLe,左右两边同时取对数得：InY=ClnKPlnLC-InA,Ine相应的数据变为：LOGYLO

3、GLLOGK8.2222044.727388B.021077.2741474.2045937.4291337.4687244.4308177.9167247.2002083.2958377.5877268.5466165.7S99608.5855377.73&01447874927.+7237072042764.060W6.0U922&4873343.4339876.&43S265.9139392.7725895.S957247.3717164.1S96&57JB2BS316.4243994.0604436.8811346.42639133322056.2461268.3959724.1108

4、74B.2J90428.6567855.5373349.0697017.4851334413S417.969827.1253395.4965087.5002206.70Q362S.76120Q7.0200217.54945-14.1100747.62664SS.2141545.4806J98L71S1913.46229354026779.13-00257.6871S64.3S20277.960S997.839S254.564J487.8421338.0218965402577B.4738477.6928575.093750088037通过Eviews软件进行回归分析得到如下结果:view!fP

5、rocIObject|printNamefFreeze|EstimateFcre-caitlitatslResids1Dependentvariable;LOGYMeihod:LeastS-quaresDate:03?25VUTime:00:33Samp-le:124-InclJdedobservations:VariableGoefliciErrtStd.Errort-StatisticFrob.LOCK0-725131O/l273915.6921700.0000LOGL0.1296530.1326470.97742603關5C-1J527SS0.561264-Z4102530.0252R-

6、squared0.892333Meandependentvar7.50783-7AdjustedR-squared0.8-32139S.D.dependentvar0.733555S.E.ofregression0.253554Akaikeinfocriterion0.209988Sumsquaredresid1.350081Schwarzcriterion0.357244Loglikelihood0.480149Hannan-Quinncrite匚0.24905-5F-statistic37.07231Durbin-Watsonstat1.387020Prob(F-siatis-tic;0.

7、000000于是得到回归方程为：IViewIIProclObject!IPrintIINjmeIIFreezeIIEstimate-1IForecast!IStatsIIResidEIEstifTiaticTLUofrirTiaudEz.LilGYLOGE：SGLCEEtirTia+icirLEiiu：ti：irL:LOGY=CCl：i+：U：iGK+CC：2：i*lJ：ii：；L+CC：3：iSlibstitutedCctfi-zierLts；LOGY=U.7513U51348*LZiSK十U.1365303292cb+DjGL十1.35278825407首先可决系数R20.892388和修

8、正的可决系数R2=0.882139都是接近于1的，故该回归方程的模拟情况还是比较好的。在5%的显著性水平下，自由度为(2,21)的F分布的临界值为F(2,21)3.47，该回归分析的统计量F=87.07231显著大于3.47,因此InY与lnK、lnL有显著0.05的关系；再看t分布，因为t(21)=1.721，其常数项p=2,410253,1.721、lnK的系数0.050P=5.692170,1.721说明这两项已经通过检验，但是lnL的回归系数没有通过检验。1这个题不知道怎么做，只能根据答案提示做出结果，具体不知道怎么分析。WaldTest:Equation:UntitledTestSt

9、atisticValuedfProbabilityt-statistic0.758171210.4563F-statistic0.57432812)0.4563Ghi-square0.57432810.4483NullHypothesis:C(2)+CJ=1NullHypothesisSummary:NormalizedRestriction(=0;Valuetd.Err.-1十G十GQ）0.4824410.636320Re-strictionsarelinearincoefficients第四章8题下表列出了某年中国部分省市城镇居民家庭平均每个全年可支配收入（X）与消费性支出（Y）的统计数据

10、。地区可支配消费性收入（地区可支配消费性收入（X）支出（Y）地区可支配消费性收入（X）支出（Y）北京10349.698493.49天津8140.56121.04北京10349.698493.49天津8140.56121.04河北5661.164348.47山西4724.113941.87内蒙古5129.053927.75辽宁5357.794356.06吉_林48104020.87黑龙江_4912.88_3824.44上_海_11718.01_8868.19江苏6800.235323.18浙江9279.167020.22山_东_6489.975022河南4766.263830.71湖北5524.

11、544644.5湖南6218.735218.79广东9761.578016.91陕_西_5124.24_4276.67甘_肃_4916.25_4126.47青_海_5169.96_4185.73新疆5644.864422.93解：最小线性二乘估计的检验结果和回归方程为：Command：L5rXcEstimaticmEquaticm:Y=C(l：l：*=X+Cl；2：l5ubst1tutedCoeic1enteY=0.805975697048*413.2S1042S541IProclobjectIIprintlINameIFreeseIEstimateIForecastIIStatsIIResi

12、dsIVietvDependentVariable:Method:LeastSquaresDate:O3J25J14Time:01:26Sample:116Includedotiservations:16VariableCoefficientStd.Errort-StatisticProbX0.805976C.02B10328.678380.0000C1128104175.-82280.0755370.9409Ft-squared0.933263Meandependentwar48.59.794AdjustedR-squared0.98206SSDdependentusr144-9.629SE

13、ofregression194.1222Alcaikeinfocriterion13.49132Sumsquaredresid527563.2Schwarzc：itEiori13.5B790Loglikelihood-105.9306Hannan-Quinneliter.13.49627F-statistic822.4779urbin-Watsonstat1.687437ProbtF-statistic0.000000异方差检验X与Y散点图，从下图可以看出方差基本一致。怀特检验结果：（这个表有些项看不懂，故也不知道怎么分析）=Equation:UNTTTLEDWorkfile:MICROSOF

14、TOFRCEEXCEL2007WORKB._XviewProcObjectPrintNameFreezeEstimateForecastStatsResidsHeterosKedastlcityTest.WhiteF-statistic1.0397UProt).F(1.U0.3252ObsR-squared1.106129Prob.Chi-Square(1;0.2929ScaledexplainedSS0.055901Prob.Chi-Square(l)0.3549TestEquation:Dependentvariable:RESIDA2Method:LeastSquaresate:0325

15、/14Time:01:43Sample:116Includedobservations:16VariableCoeffidentStd.Errort-StatisticProb.C14175.0522044.780.6430120.5306XA20.0004800.0004711.0196780.3252R-squared0.069133Meandependentwar32973.01AdjustedR-squared0002643S.D.dependentvar48415.41S.E.ofregression46351.39Akaikeinfocriterio仃24.52605Sumsqua

16、redresid3.27E+10Schwarzcriterion24.62342Loglikelihood-194.214&Hannan-Quinncrier.24.53179F-statistic1038744Durbin-Watsonstat2.178440Prob(F-：atistic)0.325179G-Q检验：两个样本的估计结果为:DependentVariable:YMethod:LeastSquaresDate:03/25/14Time:02:23Sample:16Includedobservations;6ValiableCoefficieniStd.Errort-Statis

17、ticProb.X0.8968230.4160492.15&5690.0974C-369.06702029.240-0.181B750.B645R.-squa.red0.537334Meandependentvar4003.&05AdjustedR-3quared0.421730S.D.dependentvar176.5006S.E.ofregression134.2182Akaikeinfocriterion12.39801Sumsquaredresid72053.15Schwarzcriterion1Z82860Loglikelihood-36.6940+Hannan-Quinncrite

18、r.12.6201+F-statistic4.646479Durbin-Watsonstat3.039775Prot(F-statistic)0.097371DependentVariable:YMethod:LeastSquaresDate:Q3J25；14Time:02:2SSample:16Includedobservations:6VariableCoefficientStd.Errort-StatisticProbX0.84633S0.05792114.611990.0001G-353.+640455.6136-0.7757980.4812R-squared0.9&-16-1CMea

19、ndependentvar6-103.605AdjustedR-squared0.977013S.D.dependlenivar1792.242S.E.ofregression271.7325Akaikeinfocriterion14J0872Sumsquaredresid295354.3Schwarzcriterion14.23930Loglikelihood-40.92615Hannan-Quinncriter.14.03085F-statistic213.5104urbin-Watsanstat3.270U4Protj(F-statistic)0.000128于是得到如下的F统计量：FR

20、SS1/4295354.34.09883268F(4,4)=6.39，故接受原假RSS/472058.150.052设，即不是异方差的。通过上面的检验是不存在异方差性的，不需要纠正异方差性的后果了。当然，如果显著性水平再高一点的话，该回归模型就不能通过同方差的假设性检验了，此时就需要对此进行一定的修改。权的确定这部分是看参考答案的，为什么选它目前还没完全明白。1=1Equation:UNTTTLEDWorlcfile:MICROSOFTQFFECEEX匚EL2HL._XviewProcObjectPrintNameFreezeEstimate-ForecastS-tatsResideDepen

21、dsntVariable:LOGfE)Method:LeastSquaresD3te:03J25/14Time:01:59Sample:116Includedot)sen/ations:16VariableCoefTicientStd.Errort-StatisticPrctj.X0.000535&.OOOJ101.072920C.3015C7.3533731.9424123.7SS69-10.0020R-squared0.075973Meandependentvar9.356464AdjustedR-squared0.009977S.D.dependentvar2155355S.E.ofre

22、gression2.144577Akaikeinfocriterion4.480230Sumsquaredre3id64.38392Schwarzcriterion4.57B&04LogliKelihood-33.34134Hannan-Quinncrite匚4435175F-sfaftsSc115115Durbin-Watsonstat2.505&58Prob(F-statistic)0.301454可以得到加权最小最小二乘估计的结果为：DependentVariable:YMethod:LeastSquare.sDate:03i25J14-Time:02:09Sample:116Inclu

23、dledobservaiions:16Weightingseries:WWeighttype:InversestandarddelationEVi&wsdefaultscaling)Whiteheteroskedasticity-con3istentstandarderro&covarianceVariableCoefficientStd.Errort-StatisticPrab.X0.7748750.04643116.688770.0000C1S5.7S53244.35630.7603050.4597WeighiedStatisticsR.-squared0.945729Meandepend

24、entvar4464.907AdjustedR-squared0.941S53S.D.dependentwarS53.28B4S.E.ofregression169.8362Akaikeinfocriterion13.22401Sumsquaredresid403820.5Schwarzcriterion1332059Loglikelihood-103.7921Hannan-Quinncriter.13.22396F-statistic243.9658urtin-Watsonstat1.770925Prot)(F-3tatislic)0.000000Weightedmeandep.4232.631UnweightedStatisticsR.-squared0.981692Meandependentvar4859.794AdjustedR-squared0.9803S4S.D.dependentwar1U9.629.ofregression20