计量经济学上机实验报告2

《计量经济学》上机实验报告三题目：5.3实验日期和时间：2013年11月27日星期三班级：11国贸一班学号：20111291姓名：何鹏飞实验室：103实验环境：WindowsXP;EViews3.1实验目的：掌握异方差性的检验及处理方法实验内容：建立并检验我国制造业利润函数模型实验步骤：建立模型dataxylsycxDependentVariable:YMethod:LeastSquaresDate:11/27/13Time:15:12Sample:131Includedobservations:31VariableCoefficientStd.Errort-StatisticProb.C179.1916221.57750.8087090.4253X0.7195000.04570015.744110.0000R-squared0.895260Meandependentvar3376.309AdjustedR-squared0.891649S.D.dependentvar1499.612S.E.ofregression493.6240Akaikeinfocriterion15.30377Sumsquaredresid7066274.Schwarzcriterion15.39628Loglikelihood-235.2084F-statistic247.8769Durbin-Watsonstat1.461684Prob(F-statistic)0.000000由此得回归方程y=179.1916+0.719500xt=（0.808709）（15.74411）R^2=0.895260F=247.8769异方差检验图形法相关图scatxy从图中可以看出，随着家庭人均纯收入的增加，家庭生活的消费支出不断提高，但离散程度也逐步扩大。这说明变量之间可能存在递增的异方差性。残差分析SortxLsycx在方程窗口中点击Resids按钮就可以得到模型的残差分布图图显示回归方程的残差分布有明显的扩大趋势，即表明存在异方差性。Goldfeld-Quant检验sortxsmpl112lsycx其残差平方和为413440.3，如下图所示：DependentVariable:YMethod:LeastSquaresDate:11/27/13Time:15:48Sample:112Includedobservations:12VariableCoefficientStd.Errort-StatisticProb.C1111.223409.57502.7131120.0218X0.4519000.1364283.3123780.0078R-squared0.523170Meandependentvar2453.886AdjustedR-squared0.475487S.D.dependentvar280.7556S.E.ofregression203.3323Akaikeinfocriterion13.61857Sumsquaredresid413440.3Schwarzcriterion13.69939Loglikelihood-79.71143F-statistic10.97185Durbin-Watsonstat2.030396Prob(F-statistic)0.007848smpl2031lsycx其残差平方和为5043053.如下图所示：DependentVariable:YMethod:LeastSquaresDate:11/27/13Time:15:57Sample:2031Includedobservations:12VariableCoefficientStd.Errort-StatisticProb.C-654.7683675.4844-0.9693320.3552X0.8338200.1031358.0847490.0000R-squared0.867309Meandependentvar4548.782AdjustedR-squared0.854040S.D.dependentvar1858.788S.E.ofregression710.1446Akaikeinfocriterion16.11983Sumsquaredresid5043053.Schwarzcriterion16.20064Loglikelihood-94.71896F-statistic65.36316Durbin-Watsonstat2.603102Prob(F-statistic)0.000011＝5043053/413440.3=12.197778>所以原方程确实存在异方差White检验Smpl131LsycxDependentVariable:YMethod:LeastSquaresDate:11/27/13Time:16:12Sample:131Includedobservations:31VariableCoefficientStd.Errort-StatisticProb.C179.1916221.57750.8087090.4253X0.7195000.04570015.744110.0000R-squared0.895260Meandependentvar3376.309AdjustedR-squared0.891649S.D.dependentvar1499.612S.E.ofregression493.6240Akaikeinfocriterion15.30377Sumsquaredresid7066274.Schwarzcriterion15.39628Loglikelihood-235.2084F-statistic247.8769Durbin-Watsonstat2.119816Prob(F-statistic)0.000000WhiteHeteroskedasticityTest:F-statistic7.194463Probability0.003011Obs*R-squared10.52295Probability0.005188TestEquation:DependentVariable:RESID^2Method:LeastSquaresDate:11/27/13Time:16:14Sample:131Includedobservations:31VariableCoefficientStd.Errort-StatisticProb.C69872.27641389.00.1089390.9140X-72.02221248.7240-0.2895670.7743X^20.0203370.0206270.9859720.3326R-squared0.339450Meandependentvar227944.3AdjustedR-squared0.292268S.D.dependentvar592250.3S.E.ofregression498241.3Akaikeinfocriterion29.16732Sumsquaredresid6.95E+12Schwarzcriterion29.30610Loglikelihood-449.0935F-statistic7.194463Durbin-Watsonstat2.430258Prob(F-statistic)0.003011,表明模型存在异方差性。park检验Genrlne2=log(resid^2)Genrlnx=log(x)Lslne2clnxDependentVariable:LNE2Method:LeastSquaresDate:11/27/13Time:21:45Sample:131Includedobservations:31VariableCoefficientStd.Errort-StatisticProb.C-10.186029.493410-1.0729570.2921LNX2.4313241.1395802.1335260.0415R-squared0.135668Meandependentvar10.04780AdjustedR-squared0.105864S.D.dependentvar2.521154S.E.ofregression2.383972Akaikeinfocriterion4.637754Sumsquaredresid164.8164Schwarzcriterion4.730269Loglikelihood-69.88519F-statistic4.551932Durbin-Watsonstat2.319820Prob(F-statistic)0.041468从图所示的回归结果中可以看出，lnx的系数估计值不为0且能通过显著性检验，即随即误差项的方差与解释变量存在较强的相关关系，即认为存在异方差性。Gleiser检验Genre=abs(resid)LsecxDependentVariable:EMethod:LeastSquaresDate:11/27/13Time:22:38Sample:131Includedobservations:31VariableCoefficientStd.Errort-StatisticProb.C-211.4247138.2104-1.5297310.1369X0.1149250.0285054.0316810.0004R-squared0.359179Meandependentvar299.2470AdjustedR-squared0.337082S.D.dependentvar378.1650S.E.ofregression307.9011Akaikeinfocriterion14.35978Sumsquaredresid2749290.Schwarzcriterion14.45229Loglikelihood-220.5765F-statistic16.25445Durbin-Watsonstat2.201513Prob(F-statistic)0.000367回归模型中解释变量的系数估计值显著不为0且均能通过显著性检验。所以认为存在异方差性。三、异方差性的修正（一）genrw1=1/xls(w=w1)ycxDependentVariable:YMethod:LeastSquaresDate:11/27/13Time:19:14Sample:131Includedobservations:31Weightingseries:W1VariableCoefficientStd.Errort-StatisticProb.C578.2963174.57163.3126600.0025X0.6233050.04768213.072260.0000WeightedStatisticsR-squared0.274523Meandependentvar2983.378AdjustedR-squared0.249507S.D.dependentvar373.3588S.E.ofregression323.4445Akaikeinfocriterion14.45827Sumsquaredresid3033875.Schwarzcriterion14.55079Loglikelihood-222.1032F-statistic10.97372Durbin-Watsonstat1.809428Prob(F-statistic)0.002484UnweightedStatisticsR-squared0.878889Meandependentvar3376.309AdjustedR-squared0.874712S.D.dependentvar1499.612S.E.ofregression530.8026Sumsquaredresid8170791.Durbin-Watsonstat1.891210对此修正进行white检验得WhiteHeteroskedasticityTest:F-statistic4.572942Probability0.019119Obs*R-squared7.632673Probability0.022008TestEquation:DependentVariable:STD_RESID^2Method:LeastSquaresDate:11/27/13Time:19:16Sample:131Includedobservations:31VariableCoefficientStd.Errort-StatisticProb.C244340.0179933.91.3579440.1853X-85.5377669.77649-1.2258820.2305X^20.0099380.0057871.7173280.0970R-squared0.246215Meandependentvar97866.92AdjustedR-squared0.192373S.D.dependentvar155534.2S.E.ofregression139775.5Akaikeinfocriterion26.62523Sumsquaredresid5.47E+11Schwarzcriterion26.76400Loglikelihood-409.6910F-statistic4.572942Durbin-Watsonstat2.041194Prob(F-statistic)0.019119，修正失败，仍然存在异方差性。（二）genrw1=1/x^2ls(w=w1)ycxDependentVariable:YMethod:LeastSquaresDate:11/27/13Time:19:21Sample:131Includedobservations:31Weightingseries:W2VariableCoefficientStd.Errort-StatisticProb.C787.2847173.69644.5325340.0001X0.5614720.05573110.074680.0000WeightedStatisticsR-squared0.946060Meandependentvar2743.600AdjustedR-squared0.944200S.D.dependentvar1165.059S.E.ofregression275.2095Akaikeinfocriterion14.13528Sumsquaredresid2196468.Schwarzcriterion14.22780Loglikelihood-217.0969F-statistic508.6387Durbin-Watsonstat1.700243Prob(F-statistic)0.000000UnweightedStatisticsR-squared0.848003Meandependentvar3376.309AdjustedR-squared0.842762S.D.dependentvar1499.612S.E.ofregression594.6448Sumsquaredresid10254472Durbin-Watsonstat1.541791对此模型进行white检验WhiteHeteroskedasticityTest:F-statistic4.502171Probability0.020169Obs*R-squared7.543293Probability0.023014TestEquation:DependentVariable:STD_RESID^2Method:LeastSquaresDate:11/27/13Time:19:22Sample:131Includedobservations:31VariableCoefficientStd.Errort-StatisticProb.C435027.3125153.53.4759500.0017X-137.005048.53323-2.8229120.0087X^20.0104050.0040252.5852230.0152R-squared0.243332Meandependentvar70853.80AdjustedR-squared0.189284S.D.dependentvar107975.9S.E.ofregression97221.25Akaikeinfocriterion25.89913Sumsquaredresid2.65E+11Schwarzcriterion26.03790Loglikelihood-398.4365F-statistic4.502171Durbin-Watsonstat1.150638Prob(F-statistic)0.020169，仍存在异方差性,修正失败。（三）genrw3=1/sqr(x)ls(w3)ycxDependentVariable:YMethod:LeastSquaresDate:11/27/13Time:19:27Sample:131Includedobservations:31VariableCoefficientStd.Errort-StatisticProb.C179.1916221.57750.8087090.4253X0.7195000.04570015.744110.0000R-squared0.895260Meandependentvar3376.309AdjustedR-squared0.891649S.D.dependentvar1499.612S.E.ofregression493.6240Akaikeinfocriterion15.30377Sumsquaredresid7066274.Schwarzcriterion15.39628Loglikelihood-235.2084F-statistic247.8769Durbin-Watsonstat2.119816Prob(F-statistic)0.000000对此模型进行white检验WhiteHeteroskedasticityTest:F-statistic7.194463Probability0.003011Obs*R-squared10.52295Probability0.005188TestEquation:DependentVariable:RESID^2Method:LeastSquaresDate:11/27/13Time:19:30Sample:131Includedobservations:31VariableCoefficientStd.Errort-StatisticProb.C69872.27641389.00.1089390.9140X-72.02221248.7240-0.2895670.7743X^20.0203370.0206270.9859720.3326R-squared0.339450Meandependentvar227944.3AdjustedR-squared0.292268S.D.dependentvar592250.3S.E.ofregression498241.3Akaikeinfocriterion29.16732Sumsquaredresid6.95E+12Schwarzcriterion29.30610Loglikelihood-449.0935F-statistic7.194463Durbin-Watsonstat2.430258Prob(F-statistic)0.003011，仍然存在异方差性。（四）根据park检验结果Genrw4=1/x^2.431324Ls(w4)ycxDependentVariable:YMethod:LeastSquaresDate:11/27/13Time:22:01Sample:131Includedobservations:31VariableCoefficientStd.Errort-StatisticProb.C179.1916221.57750.8087090.4253X0.7195000.04570015.744110.0000R-squared0.895260Meandependentvar3376.309AdjustedR-squared0.891649S.D.dependentvar1499.612S.E.ofregression493.6240Akaikeinfocriterion15.30377Sumsquaredresid7066274.Schwarzcriterion15.39628Loglikelihood-235.2084F-statistic247.8769Durbin-Watsonstat1.461684Prob(F-statistic)0.000000WhiteHeteroskedasticityTest:F-statistic7.194463Probability0.003011Obs*R-squared10.52295Probability0.005188TestEquation:DependentVariable:RESID^2Method:LeastSquaresDate:11/27/13Time:22:01Sample:131Includedobservations:31VariableCoefficientStd.Errort-StatisticProb.C69872.27641389.00.1089390.9140X-72.02221248.7240-0.2895670.7743X^20.0203370.0206270.9859720.3326R-squared0.339450Meandependentvar227944.3AdjustedR-squared0.292268S.D.dependentvar592250.3S.E.ofregression498241.3Akaikeinfocriterion29.16732Sumsquaredresid6.95E+12Schwarzcriterion29.30610Loglikelihood-449.0935F-statistic7.194463Durbin-Watsonstat2.390409Prob(F-statistic)0.003011，仍然存在异方差性。（五）genrw5=1/abs(resid)ls(w=w5)ycxDependentVariable:YMethod:LeastSquaresDate:11/27/13Time:22:44Sample:131Includedobservations:31Weightingseries:W5VariableCoefficientStd.Errort-StatisticProb.C272.773831.349028.7011900.0000X0.6965120.00809985.998460.0000WeightedStatisticsR-squared0.999696Meandependentvar2971.214AdjustedR-squared0.999685S.D.dependentvar3736.797S.E.ofregression66.27406Akaikeinfocriterion11.28781Sumsquaredresid127375.3S