计量经济学上机实验报告(异方差性).doc

计量经济学上机实验报告三题目：检验异方差性 实验日期和时间： 班级： 学号：姓名实验室：实验环境： Windows XP ; EViews 3.1实验目的：掌握异方差性的检验及处理方法实验内容：建立并检验四川省农村人均纯收入（X）与人均生活费支出（Y）的函数模型实验步骤：一：检验异方差性图形分析检验观察销售利润（Y）与销售收入（X）的相关图(图1)：SCAT X Y从图中可以看出，随着农村人均纯收入的增加，人均消费支出不断提高，但是离散程度不大，这不能说明变量之间可能存在递增的异方差性，需要进行别的检验方法。残差分析首先将数据排序（命令格式为：SORT 解释变量），然后建立回归方程。在方程窗口中点击Resids按钮就可以得到模型的残差分布图（或建立方程后在Eviews工作文件窗口中点击resid对象来观察）。上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检验将样本安解释变量排序（SORT X）并分成两部分（分别有1978到1988共11个样本合1998到2008共11个样本）利用样本1建立回归模型1（回归结果如图3），其残差平方和为1285.598。SMPL 1978 1988LS Y C XDependent Variable: YMethod: Least SquaresDate: 11/25/13 Time: 08:53Sample: 1978 1988Included observations: 11VariableCoefficientStd. Errort-StatisticProb. C-19.6653411.22945-1.7512290.1138X0.9699700.03946624.577610.0000R-squared0.985320 Mean dependent var241.7309Adjusted R-squared0.983688 S.D. dependent var93.57987S.E. of regression11.95175 Akaike info criterion7.962598Sum squared resid1285.598 Schwarz criterion8.034942Log likelihood-41.79429 F-statistic604.0588Durbin-Watson stat0.823583 Prob(F-statistic)0.000000利用样本2建立回归模型2（回归结果如图4），其残差平方和为29558.82。SMPL 1998 2008LS Y C XDependent Variable: YMethod: Least SquaresDate: 11/25/13 Time: 08:55Sample: 1998 2008Included observations: 11VariableCoefficientStd. Errort-StatisticProb. C54.5240062.053210.8786650.4024X0.7590360.02353432.252280.0000R-squared0.991422 Mean dependent var1976.724Adjusted R-squared0.990469 S.D. dependent var587.0207S.E. of regression57.30893 Akaike info criterion11.09776Sum squared resid29558.82 Schwarz criterion11.17010Log likelihood-59.03766 F-statistic1040.209Durbin-Watson stat1.124021 Prob(F-statistic)0.000000计算F统计量：29558.82/1285.598=22.992，分别是模型1和模型2的残差平方和。取时，查F分布表得F0.05(11-1-1,11-1-1)=3.18，而F=22.992 F0.05(11-1-1,11-1-1)=3.18，所以存在异方差性White检验建立回归模型：LS Y C X，回归结果如图Dependent Variable: YMethod: Least SquaresDate: 11/25/13 Time: 08:50Sample: 1978 2008Included observations: 31VariableCoefficientStd. Errort-StatisticProb. C71.6140718.157593.9440290.0005X0.7614450.01088169.982270.0000R-squared0.994113 Mean dependent var1030.406Adjusted R-squared0.993911 S.D. dependent var850.2099S.E. of regression66.34631 Akaike info criterion11.28999Sum squared resid127653.1 Schwarz criterion11.38251Log likelihood-172.9949 F-statistic4897.518Durbin-Watson stat0.401829 Prob(F-statistic)0.000000在方程窗口上点击ViewResidual Tests Heteroskedastcity Tests ,在弹出的对话框中选择White,并点击OK,检验结果如图White Heteroskedasticity Test:F-statistic1.631313 Probability0.213725Obs*R-squared3.235218 Probability0.198372Test Equation:Dependent Variable: RESID2Method: Least SquaresDate: 11/25/13 Time: 09:02Sample: 1978 2008Included observations: 31VariableCoefficientStd. Errort-StatisticProb. C730.63222245.2710.3254090.7473X6.0093293.3491881.7942640.0836X2-0.0015010.000910-1.6483110.1105R-squared0.104362 Mean dependent var4117.843Adjusted R-squared0.040388 S.D. dependent var6279.224S.E. of regression6151.116 Akaike info criterion20.37842Sum squared resid1.06E+09 Schwarz criterion20.51719Log likelihood-312.8655 F-statistic1.631313Durbin-Watson stat0.883445 Prob(F-statistic)0.213725其中F值为辅助回归模型的F统计量值。取显著水平，由于0.05(2)=5.993.235218,所以不存在异方差性。实际应用中可以直接观察相伴概率p值的大小，若p值较小，则认为存在异方差性。反之，则认为不存在异方差性。Park检验建立回归模型（结果同图5所示）。生成新变量序列：GENR LNE2=log(RESID2)GENR LNX=log(X)建立新残差序列对解释变量的回归模型：LS LNE2 C LNX，回归结果如图7所示。Dependent Variable: LNE2Method: Least SquaresDate: 11/25/13 Time: 09:05Sample: 1978 2008Included observations: 31VariableCoefficientStd. Errort-StatisticProb. C4.9412511.8100772.7298560.0107LNX0.3688680.2677471.3776730.1788R-squared0.061427 Mean dependent var7.406587Adjusted R-squared0.029063 S.D. dependent var1.538091S.E. of regression1.515576 Akaike info criterion3.731808Sum squared resid66.61211 Schwarz criterion3.824324Log likelihood-55.84303 F-statistic1.897982Durbin-Watson st