江西农业大学经济贸易学院学生实验报告课程名称：计量经济学专业班级：经济1201班姓名：学号：指导教师：徐冬梅职称：讲师实验日期： 2014.12.11学生实验报告一、实验目的及要求1、目的会使用EVIEWS对计量经济模型进行分析2、内容及要求（1）对经典线形回归模型进行参数估计、参数的检验与区间估计，对模型总体进行显著性检验；（2）异方差的检验及其处理；（3）自相关的检验及其处理；（4）多重共线性检验及其处理；二、仪器用具三、实验方法与步骤(一)数据的输入、描述及其图形处理；(二)方程的估计；(三)参数的检验、违背经典假定的检验；(四)模型的处理与预测四、实验结果与数据处理实验一：中国城镇居民人均消费支出模型数据散点图：通过Eviews 估计参数方程 回归方程：Dependent Variable: Y Method: Least Squares Date: 11/27/14 Time: 15:02 Sample: 1 31Included observations: 31VariableCoefficien tStd. Error t-StatisticProb.X 1.359477 0.043302 31.39525 0.0000 C-57.90655377.7595-0.1532890.8792 R-squared0.971419 Mean dependent var 11363.69 Adjusted R-squared 0.970433 S.D. dependent var 3294.469 S.E. of regression 566.4812 Akaike infocriterion15.57911 Sum squared resid 9306127. Schwarz criterion 15.67162 Log likelihood -239.4761 F-statistic 985.6616 Durbin-Watson stat1.294974 Prob(F-statistic)0.000000 5000100001500020000250006000800010000120001400016000XY得出估计方程为：Y = 1.35947661442*X - 57.9065479515 异方差检验 1、图示检验法图形呈现离散趋势，大致判断存在异方差性。

2、Park 检验Dependent Variable: LOG(E2) Method: Least Squares Date: 11/27/14 Time: 16:16 Sample: 1 31Included observations: 31VariableCoefficien t Std. Error t-StatisticProb.C 19.82562 19.85359 0.998591 0.3263 LOG(X)-0.9564032.204080 -0.4339240.6676 R-squared0.006451 Mean dependent var 11.21371 Adjusted R-squared -0.027809 S.D. dependent var 2.894595 S.E. of regression 2.934568 Akaike infocriterion5.053338 Sum squared resid 249.7389 Schwarz criterion 5.145854 Log likelihood -76.32674 F-statistic 0.188290 Durbin-Watson stat2.456500 Prob(F-statistic)0.6675550500000100000015000006000800010000120001400016000XE 2看到图中LOG(E2)中P值为0.6676 > 0.05，所以不存在异方差性3、G-Q检验检验：e1Dependent Variable: XMethod: Least SquaresDate: 11/27/14 Time: 16:41Sample: 1 12Included observations: 12Std. Error t-Statistic Prob.Variable CoefficientC4642.0282014.183 2.3046710.0439Y0.2310460.215824 1.0705300.3095R-squared0.102820 Mean dependent var6796.390 Adjusted R-squared0.013102 S.D. dependent var293.276214.33793S.E. of regression291.3486 Akaike infocriterionSum squared resid848840.2 Schwarz criterion14.41875Log likelihood-84.02758 F-statistic 1.146034 Durbin-Watson stat0.445146 Prob(F-statistic)0.309538e检验：2Dependent Variable: XMethod: Least SquaresDate: 11/27/14 Time: 16:42Sample: 20 31Included observations: 12Std. Error t-Statistic Prob.Variable CoefficientC583.4526593.43700.9831750.3487Y0.6977480.04019617.358700.0000R-squared0.967879 Mean dependent var10586.89 Adjusted R-squared0.964667 S.D. dependent var2610.864S.E. of regression490.7655 Akaike info 15.38082criterionSum squared resid2408507. Schwarz criterion15.46164Log likelihood-90.28493 F-statistic301.3245Durbin-Watson stat 2.748144 Prob(F-statistic)0.000000第一个图中的残差平方和为848840.2第二个图中的残差平方和为2408507所以F值为2408507/848840.2 = 2.8374 < 2.97，所以不存在异方差性4、White检验White Heteroskedasticity Test:F-statistic 2.240402 Probability0.125152Obs*R-squared 4.276524 Probability0.117860Test Equation:Dependent Variable: RESID^2Method: Least SquaresDate: 11/27/14 Time: 16:50Sample: 1 31Included observations: 31Variable CoefficienStd. Error t-Statistic Prob.tC-2135113.1158576.-1.8428760.0760X503.7331242.2078 2.0797560.0468X^2-0.0236090.011650-2.0265900.0523R-squared0.137952 Mean dependent var300197.6Adjusted R-squared0.076378 S.D. dependent var347663.4S.E. of regression334122.9 Akaike info28.36817criterionSum squared resid 3.13E+12 Schwarz criterion28.50694Log likelihood-436.7067 F-statistic 2.240402Durbin-Watson stat 1.871252 Prob(F-statistic)0.125152P值为0.11786 > 0.05，所以不存在异方差性通过四种不同的检验得知除了图示检验法得出异方差的结论，其他的检验的结论都是不存在异方差的。

5、WLS（加权最小二乘法）修正Dependent Variable: YMethod: Least SquaresDate: 11/27/14 Time: 17:14Sample: 1 31Included observations: 31Weighting series: E3Std. Error t-Statistic Prob.Variable CoefficientC-85.6942624.15675-3.5474250.0013X 1.3622210.002307590.56150.0000Weighted StatisticsR-squared 1.000000 Mean dependent var13474.53Adjusted R-squared 1.000000 S.D. dependent var61353.749.559810S.E. of regression27.93264 Akaike infocriterionSum squared resid22626.73 Schwarz criterion9.652325Log likelihood-146.1770 F-statistic348762.9Durbin-Watson stat 2.061818 Prob(F-statistic)0.000000UnweightedStatisticsR-squared0.971413 Mean dependent var11363.69Adjusted R-squared0.970427 S.D. dependent var3294.469S.E. of regression566.5415 Sum squared resid9308110.Durbin-Watson stat 2.178992实验二：中国粮食生产函数1、回归方程Dependent Variable: LOG(Y)Method: Least SquaresDate: 12/11/14 Time: 15:06Sample: 1983 2007Included observations: 25Variable CoefficienStd. Error t-Statistic Prob.tLOG(X1)0.3811450.0502427.5861820.0000LOG(X2) 1.2222890.1351799.0420300.0000LOG(X3)-0.0811100.015304-5.3000240.0000LOG(X4)-0.0472290.044767-1.0549800.3047LOG(X5)-0.1011740.057687-1.7538530.0956C-4.173174 1.923624-2.1694340.0429R-squared0.981597 Mean dependent var10.70905Adjusted R-squared0.976753 S.D. dependent var0.093396-5.459968S.E. of regression0.014240 Akaike infocriterionSum squared resid0.003853 Schwarz criterion-5.167438Log likelihood74.24960 F-statistic202.6826Durbin-Watson stat 1.791427 Prob(F-statistic)0.000000得出回归方程为：LOG(Y) = 0.381144581612*LOG(X1) + 1.22228859801*LOG(X2) - 0.0811098881534*LOG(X3) - 0.0472********LOG(X4) - 0.101173736285*LOG(X5) - 4.173********通过检验结果可知R2 较大且接近于1，而且F=202.6826 > F0.05(5,19) = 2.74，故认为粮食产量与上述变量之间总体线性关系显著。