计量经济学实验报告成绩课程名称计量经济学指导教师苏卫东实验日期 2014-6-24 院（系）财政与金融学院专业班级金融二专实验地点实验楼八机房学生姓名单一芳学号 201212041018 同组人无实验项目名称多重共线性的检验与修正一、实验目的和要求1、理解多重共线性的含义与后果2、掌握Eviews软件的操作和多重共线性的检验与修正二、实验原理Eviews软件的操作和多重共线性的检验修正方法三、主要仪器设备、试剂或材料Eviews软件，计算机四、实验方法与步骤1、准备工作：建立工作文件，并输入数据CREATE A 1974 1981;DATA Y X1 X2 X3 X4 X52、OLS估计：LS Y C X1 X2 X3 X4 X5;3、计算简单相关系数COR X1 X2 X3 X4 X54、多重共线性的解决LS Y C X1;LS Y C X2;LS Y C X3;LS Y C X4;LS Y C X5;LS Y C X1 X3;LS Y C X1 X3 X2;LS Y C X1 X3 X4;LS Y C X1 X3 X5五、实验数据记录、处理及结果分析1、建立工作组，输入以下数据：obs Y X1 X2 X3 X4 X5 1974 98.45 560.2 153.2 6.53 1.23 1.89 1975 100.7 603.11 190 9.12 1.3 2.03 1976 102.8 668.05 240.3 8.1 1.8 2.71 1977 133.95 715.47 301.12 10.1 2.09 3 1978 140.13 724.27 361 10.93 2.39 3.29 1979 143.11 736.13 420 11.85 3.9 5.24 1980 146.15 748.91 497.16 12.28 5.13 6.83 1981 144.6 760.32 501 13.5 5.47 8.36 1982 148.94 774.92 529.2 15.29 6.09 10.07 1983 158.55 785.3 552.72 18.1 7.97 12.57 1984 169.68 795.5 771.16 19.61 10.18 15.12 1985 162.14 804.8 811.8 17.22 11.79 18.25 1986 170.09 814.94 988.43 18.6 11.54 20.59 1987 178.69 828.73 1094.65 23.53 11.68 23.372、OLS估计LS Y C X1 X2 X3 X4 X5Dependent Variable: YMethod: Least SquaresDate: 06/24/14 Time: 18:45Sample: 1974 1987Included observations: 14Variable Coefficient Std. Error t-Statistic Prob.C -3.650950 30.00144 -0.121692 0.9061X1 0.125752 0.059087 2.128275 0.0660X2 0.072656 0.037445 1.940317 0.0883X3 2.681426 1.258639 2.130418 0.0658X4 3.405866 2.444896 1.393052 0.2011X5 -4.430561 2.194164 -2.019248 0.0781R-squared 0.970397 Mean dependent var 142.7129Adjusted R-squared 0.951896 S.D. dependent var 26.09805S.E. of regression 5.724011 Akaike info criterion 6.624744Sum squared resid 262.1144 Schwarz criterion 6.898625Log likelihood -40.37320 F-statistic 52.44910Durbin-Watson stat 1.965760 Prob(F-statistic) 0.000007用Eviews进行最小二乘估计得，Yˆ=-3.497+0.125X1+0.074X2+2.678X3+3.453X4-4.491X5(-0.1) (2.1) (1.9) (2.1) (1.4) (-2.0)R2=0.970, 2R=0.952, DW=1.97, F=52.53其中括号内的数字是t值。

给定显著水平α=0.05，回归系数估计值都没有显著性。

查F分布表，得临界值为F0.05（5,8）=3.69，故F=52.53>3.69，回归方程显著。

3、计算简单相关系数COR X1 X2 X3 X4 X5X1 X2 X3 X4 X5 X1 1.000000 0.866910 0.882293 0.852449 0.821305 X2 0.866910 1.000000 0.945587 0.964667 0.982176 X3 0.882293 0.945587 1.000000 0.940506 0.948361 X4 0.852449 0.964667 0.940506 1.000000 0.981979 X5 0.821305 0.982176 0.948361 0.981979 1.000000r12=0.867，r13=0.882，r14=0.852，r15=0.821，r23=0.946，r24=0.965, r25=0.983，r34=0.941，r35=0.948，r45=0.982可见解释变量之间是高度相关的。

4、多重共线性的解决, 采用Frisch法。

对Y关于X1，X2，X3，X4，X5作最小二乘回归:（1） LS Y C X1Dependent Variable: YMethod: Least SquaresDate: 06/24/14 Time: 18:57Sample: 1974 1987Included observations: 14Variable Coefficient Std. Error t-Statistic Prob.C -90.92074 19.32929 -4.703781 0.0005X1 0.316925 0.026081 12.15161 0.0000R-squared 0.924841 Mean dependent var 142.7129Adjusted R-squared 0.918578 S.D. dependent var 26.09805S.E. of regression 7.446964 Akaike info criterion 6.985054Sum squared resid 665.4873 Schwarz criterion 7.076347Log likelihood -46.89537 F-statistic 147.6617Durbin-Watson stat 1.536885 Prob(F-statistic) 0.000000得回归方程为：Yˆ=-90.921+0.317X1（-4.7）（12.2)R2=0.925, 2R=0.919, DW=1.537, F=147.619(2)LS Y C X2Dependent Variable: YMethod: Least SquaresDate: 06/24/14 Time: 19:01Sample: 1974 1987Included observations: 14Variable Coefficient Std. Error t-Statistic Prob.C 99.55849 6.424119 15.49761 0.0000X2 0.081514 0.010720 7.603722 0.0000R-squared 0.828121 Mean dependent var 142.7129 Adjusted R-squared 0.813798 S.D. dependent var 26.09805 S.E. of regression 11.26161 Akaike info criterion 7.812239 Sum squared resid 1521.885 Schwarz criterion 7.903533 Log likelihood -52.68568 F-statistic 57.81659 Durbin-Watson stat 0.641926 Prob(F-statistic) 0.000006得回归方程为：Yˆ=99.614+0.0815X2（15.5）（7.6）R2=0.828, 2R=0.813, DW=0.639，F=57.564(3) LS Y C X3Dependent Variable: YMethod: Least SquaresDate: 06/24/14 Time: 19:04Sample: 1974 1987Included observations: 14Variable Coefficient Std. Error t-Statistic Prob.C 74.64824 8.288989 9.005711 0.0000X3 4.892712 0.563578 8.681514 0.0000R-squared 0.862651 Mean dependent var 142.7129 Adjusted R-squared 0.851205 S.D. dependent var 26.09805 S.E. of regression 10.06704 Akaike info criterion 7.587974 Sum squared resid 1216.144 Schwarz criterion 7.679268 Log likelihood -51.11582 F-statistic 75.36868 Durbin-Watson stat 0.813884 Prob(F-statistic) 0.000002得回归方程为：Yˆ=74.648+4.893X3（9.0）（8.7）R2=0.863, 2R=0.851, DW=0.814，F=75.369Dependent Variable: YMethod: Least SquaresDate: 06/24/14 Time: 19:07Sample: 1974 1987Included observations: 14Variable Coefficient Std. Error t-Statistic Prob.C 108.8647 5.934330 18.34490 0.0000X4 5.739752 0.838756 6.843175 0.0000R-squared 0.796019 Mean dependent var 142.7129 Adjusted R-squared 0.779021 S.D. dependent var 26.09805 S.E. of regression 12.26828 Akaike info criterion 7.983475 Sum squared resid 1806.129 Schwarz criterion 8.074769 Log likelihood -53.88433 F-statistic 46.82904 Durbin-Watson stat 0.769006 Prob(F-statistic) 0.000018得回归方程为：Yˆ=108.865+5.740X4（18.3）（6.8）R2=0.796, 2R=0.779, DW=0.769，F=46.829Dependent Variable: YMethod: Least SquaresDate: 06/24/14 Time: 19:12Sample: 1974 1987Included observations: 14Variable Coefficient Std. Error t-Statistic Prob.C 113.3747 6.077133 18.65596 0.0000X5 3.080811 0.512300 6.013688 0.0001R-squared 0.750854 Mean dependent var 142.7129 Adjusted R-squared 0.730091 S.D. dependent var 26.09805S.E. of regression 13.55865 Akaike info criterion 8.183490 Sum squared resid 2206.044 Schwarz criterion 8.274784Log likelihood -55.28443 F-statistic 36.16444 Durbin-Watson stat 0.593639 Prob(F-statistic) 0.000061得回归方程为：Yˆ=113.375+3.081X5（18.7）（6.0）(1)加入肉销售量X3，对Y关于X1，X3作最小二乘回归 LS Y C X1 X3 Dependent Variable: YMethod: Least SquaresDate: 06/24/14 Time: 19:17Sample: 1974 1987Included observations: 14Variable Coefficient Std. Error t-Statistic Prob.C -39.79479 25.01570 -1.590793 0.1400X1 0.211543 0.045302 4.669581 0.0007X3 1.909246 0.724153 2.636523 0.0231R-squared 0.953945 Mean dependent var 142.7129 Adjusted R-squared 0.945571 S.D. dependent var 26.09805 S.E. of regression 6.088671 Akaike info criterion 6.638146 Sum squared resid 407.7910 Schwarz criterion 6.775087 Log likelihood -43.46702 F-statistic 113.9220 Durbin-Watson stat 1.655554 Prob(F-statistic) 0.000000得回归方程为：Yˆ=-39.795+0.212X1+1.909X3（-1.6）（4.7）(2.6)R2=0.954, 2R=0.946, DW=1.656，F=113.922可以看出，加入X3后，拟合优度R2和2R均有所增加，参数估计值的符号也正确，并且没有影响X1系数的显著性，所以在模型中保留X3.(2)加入人均收入X2，对Y关于X1,X2,X3作最小二乘回归LS Y C X1 X3 X2Dependent Variable: YMethod: Least SquaresDate: 06/24/14 Time: 19:20Sample: 1974 1987Included observations: 14Variable Coefficient Std. Error t-Statistic Prob.C -34.64352 27.82008 -1.245270 0.2414X1 0.206349 0.048015 4.297629 0.0016X3 1.449336 1.175819 1.232619 0.2459X2 0.009588 0.018880 0.507819 0.6226R-squared 0.955103 Mean dependent var 142.7129Adjusted R-squared 0.941633 S.D. dependent var 26.09805S.E. of regression 6.305072 Akaike info criterion 6.755542Sum squared resid 397.5393 Schwarz criterion 6.938130Log likelihood -43.28879 F-statistic 70.91010Durbin-Watson stat 1.682715 Prob(F-statistic) 0.000000Yˆ=-34.777+0.207X1+0.009X2+1.456X3(-1.3) 4.3) (0.5) (1.2)R2=0.955, 2R=0.942, DW=1.683, F=70.839可以看出，再加入X2后，拟合优度R2增加不显著，2R有所减小，并且X2和X3系数均不显著，说明存在严重的共线性。