多重共线性的检验与修正

附件二：实验报告格式（首页）山东轻工业学院实验报告成绩课程名称计量经济学指导教师实验日期2013-5-25院（系）商学院专业班级实验地点二机房学生姓名学号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：CREATEEX-7-1A19741981;TATAYX1X2X3X4X5;(2)OLS估计：LSYCX1X2X3X4X5;(3)计算简单相关系数CORX1X2X3X4X5;(4)多重共线性的解决LSYCX1;LSYCX2;LSYCX3;LSYCX4;LSYCX5;LSYCX1X3;LSYCX1X3X2;LSYCX1X3X4;LSYCX1X3X5;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：98.45560.20153.206.531.231.89100.70603.11190.009.121.302.03102.80668.05240.308.101.802.71133.95715.47301.1210.102.093.00140.13724.27361.0010.932.393.29143.11736.13420.0011.853.905.24146.15748.91491.7612.285.136.83144.60760.32501.0013.505.478.36148.94774.92529.2015.296.0910.07158.55785.30552.7218.107.9712.57169.68795.50771.1619.6110.1815.12162.14804.80811.8017.2211.7918.25170.09814.94988.4318.6011.5420.59178.69828.731094.6523.5311.6823.37（2）OLS估计DependentVariable:YMethod:LeastSquaresDate:05/25/13Time:11:10Sample:19741987Includedobservations:14VariableCoefficientStd.Errort-StatisticProb.C-3.49656330.00659-0.1165260.9101X10.1253300.0591392.1192450.0669X20.0736670.0378771.9448970.0877X32.6775891.2572932.1296460.0658X43.4534482.4508501.4090820.1965X5-4.4911172.214862-2.0277190.0771R-squared0.970442Meandependentvar142.7129AdjustedR-squared0.951968S.D.dependentvar26.09805S.E.ofregression5.719686Akaikeinfocriterion6.623232Sumsquaredresid261.7185Schwarzcriterion6.897114Loglikelihood-40.36262F-statistic52.53086Durbin-Watsonstat1.972755Prob(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=52.533.69，回归方程显著。(3)计算简单相关系数CORX1X2X3X4X5;X1X2X3X4X5X110.866551867279170.8822931086064990.8524491353193940.821305444858646X20.8665518672791710.9458956983200270.9647730220121920.98253206329193X30.8822931086064990.94589569832002710.9405058208239960.948361346495427X40.8524491353193940.9647730220121920.94050582082399610.98197917741363X50.8213054448586460.982532063291930.9483613464954270.981979177413631r12=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。&1.对Y关于X1，X2，X3，X4，X5作最小二乘回归:1)LSYCX1DependentVariable:YMethod:LeastSquaresDate:05/25/13Time:11:12Sample:19741987Includedobservations:14VariableCoefficientStd.Errort-StatisticProb.C-90.9207419.32929-4.7037810.0005X10.3169250.02608112.151610.0000R-squared0.924841Meandependentvar142.7129AdjustedR-squared0.918578S.D.dependentvar26.09805S.E.ofregression7.446964Akaikeinfocriterion6.985054Sumsquaredresid665.4873Schwarzcriterion7.076347Loglikelihood-46.89537F-statistic147.6617Durbin-Watsonstat1.536885Prob(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=147.6192)LSYCX2DependentVariable:YMethod:LeastSquaresDate:05/25/13Time:11:14Sample:19741987Includedobservations:14VariableCoefficientStd.Errort-StatisticProb.C99.613496.43124215.489000.0000X20.0814700.0107387.5871190.0000R-squared0.827498Meandependentvar142.7129AdjustedR-squared0.813123S.D.dependentvar26.09805S.E.ofregression11.28200Akaikeinfocriterion7.815858Sumsquaredresid1527.403Schwarzcriterion7.907152Loglikelihood-52.71101F-statistic57.56437Durbin-Watsonstat0.638969Prob(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=57.5643）LSYCX3DependentVariable:YMethod:LeastSquaresDate:05/25/13Time:11:14Sample:19741987Includedobservations:14VariableCoefficientStd.Errort-StatisticProb.C74.648248.2889899.0057110.0000X34.8927120.5635788.6815140.0000R-squared0.862651Meandependentvar142.7129AdjustedR-squared0.851205S.D.dependentvar26.09805S.E.ofregression10.06704Akaikeinfocriterion7.587974Sumsquaredresid1216.144Schwarzcriterion7.679268Loglikelihood-51.11582F-statistic75.36868Durbin-Watsonstat0.813884Prob(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=75.3694)LSYCX4DependentVariable:YMethod:LeastSquaresDate:05/25/13Time:11:15Sample:19741987Includedobservations:14VariableCoefficientStd.Errort-StatisticProb.C108.86475.93433018.344900.0000X45.7397520.8387566.8431750.0000R-squared0.796019Meandependentvar142.7129AdjustedR-squared0.779021S.D.dependentvar26.09805S.E.ofregression12.26828Akaikeinfocriterion7.983475Sumsquaredresid1806.129Schwarzcriterion8.074769Loglikelihood-53.88433F-statistic46.82904Durbin-Watsonstat0.769006Prob(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=46.8295)LSYCX5DependentVariable:YMethod:LeastSquaresDate:05/25/13Time:11:16Sample:19741987Includedobservations:14VariableCoefficientStd.Errort-StatisticProb.C113.37476.07713318.655960.0000X53.0808110.5123006.0136880.0001R-squared0.750854Meandependentvar142.7129AdjustedR-squared0.730091S.D.dependentvar26.09805S.E.ofregression13.55865Akaikeinfocriterion8.183490Sumsquaredresid2206.044Schwarzcriterion8.274784Loglikelihood-55.28443F-statistic36.16444Durbin-Watsonstat0.593639Prob(F-statistic