多重共线性实验报告 lucas(共8页)

1、PAGE PAGE 8计量经济学实验报告 四 开课(ki k)实验室： 崇德楼315 2013年 5月19日姓 名金超龙成 绩年级专业2010级国贸专业学 号20102811课程名称计量经济学实验名称多重共线性实验一、实验内容 依据经济学理论，以实际数据（实验数据五）为基础，建立反映天津市粮食市场需求状况的粮食需求函数。检验所建立的粮食需求函数是否存在多重共线性。如果存在多重共线性，使用恰当的方法加以解决。二、实验目的 熟练使用EViews软件进行计量分析，理解多重共线性的检验和估计的基本方法。三、实验步骤STEP1：参数估计STEP2：检验STEP3：消除多重共线性四、实验结果及分析（附上必

2、要的回归分析报告，并作以分析）经分析，影响天津粮食需求的主要因素，除了市常住人口和人均收入以外，还可能与相关其他农畜产品有关。为此，考虑的影响因素主要有市常住人口X1，人均收入X2、肉销售量X3、蛋销售量X4和鱼虾销售量X5。为此设定如下的对数形式的计量经济模型：Y=粮食销售量（万吨/年）；X1=市常住人口数（万人）；X2=人均收入（元/年）；X3=肉销售量（万吨/年）；X4=蛋销售量（万吨/年）；X5=鱼虾销售量（万吨/年）。数据见实验指导数据五，来源于中国统计年鉴年STEP1：参数估计在Eviews中点击NEW项，建立Workfile输入Y、X1、X2、X3、X4、X5的数据。点击Quic

3、k，选Estimate Equation项，在OLS对话框中，键入Y C X1 X2 X3 X4 X5，输出结果。见图6.4.1。图6.4.1 Eviews输出的回归结果分析：模型R2=0.970391 可决系数很高，F检验值52.43740，显著。但当=5%时，t统计值=1.7613，X4和X5系数的t检验不显著，同时X5的系数为负号不符合实际，这表明很可能存在多重共线性。STEP2：检验计算各解释变量的相关系数，选择X1、X2、X3、X4、X5数据，点击“quickgroup statisticscorrelation”的相关系数矩阵，见表6.4.1。有相关系数矩阵可以看出：各解释变量相关

4、之间的相关系数较高，证实存在严重多重共线性。表6.4.1 自变量相关系数矩阵STEP3：消除多重共线性采用逐步回归的办法，检验和解决多重共线性问题。分别作Y对X1、X2、X3、X4、X5的一元回归，结果如表6.4.2。Dependent Variable: YMethod: Least SquaresDate: 05/12/03 Time: 13:56Sample: 1974 1987Included observations: 14VariableCoefficientStd. Errort-StatisticProb.C-90.9207419.32929-4.7037810.0005X10

5、.3169250.02608112.151610.0000R-squared0.924841Mean dependent var142.7129Adjusted R-squared0.918578S.D. dependent var26.09805S.E. of regression7.446964Akaike info criterion6.985054Sum squared resid665.4873Schwarz criterion7.076347Log likelihood-46.89537F-statistic147.6617Durbin-Watson stat1.536885Pro

6、b(F-statistic)0.000000Dependent Variable: YMethod: Least SquaresDate: 05/12/03 Time: 13:59Sample: 1974 1987Included observations: 14VariableCoefficientStd. Errort-StatisticProb.C99.552516.42336415.498500.0000X20.0815190.0107187.6055060.0000R-squared0.828188Mean dependent var142.7129Adjusted R-square

7、d0.813870S.D. dependent var26.09805S.E. of regression11.25942Akaike info criterion7.811851Sum squared resid1521.294Schwarz criterion7.903145Log likelihood-52.68296F-statistic57.84372Durbin-Watson stat0.642278Prob(F-statistic)0.000006Dependent Variable: YMethod: Least SquaresDate: 05/12/03 Time: 14:0

8、0Sample: 1974 1987Included observations: 14VariableCoefficientStd. Errort-StatisticProb.C74.648248.2889899.0057110.0000X34.8927120.5635788.6815140.0000R-squared0.862651Mean dependent var142.7129Adjusted R-squared0.851205S.D. dependent var26.09805S.E. of regression10.06704Akaike info criterion7.58797

9、4Sum squared resid1216.144Schwarz criterion7.679268Log likelihood-51.11582F-statistic75.36868Durbin-Watson stat0.813884Prob(F-statistic)0.000002Dependent Variable: YMethod: Least SquaresDate: 05/12/03 Time: 14:01Sample: 1974 1987Included observations: 14VariableCoefficientStd. Errort-StatisticProb.C

10、108.86475.93433018.344900.0000X45.7397520.8387566.8431750.0000R-squared0.796019Mean dependent var142.7129Adjusted R-squared0.779021S.D. dependent var26.09805S.E. of regression12.26828Akaike info criterion7.983475Sum squared resid1806.129Schwarz criterion8.074769Log likelihood-53.88433F-statistic46.8

11、2904Durbin-Watson stat0.769006Prob(F-statistic)0.000018Dependent Variable: YMethod: Least SquaresDate: 05/12/03 Time: 14:02Sample: 1974 1987Included observations: 14VariableCoefficientStd. Errort-StatisticProb.C113.37476.07713318.655960.0000X53.0808110.5123006.0136880.0001R-squared0.750854Mean depen

12、dent var142.7129Adjusted R-squared0.730091S.D. dependent var26.09805S.E. of regression13.55865Akaike info criterion8.183490Sum squared resid2206.044Schwarz criterion8.274784Log likelihood-55.28443F-statistic36.16444Durbin-Watson stat0.593639Prob(F-statistic)0.000061表6.4.2 回归结果变 量X1X2X3X4X5参数估计值0.316

13、9250.0815194.8927125.7397523.080811t统计值12.151617.6055068.6815146.8431756.013688R20.9248410.8281880.8626510.7960190.750854按R2的大小排序为：X1、X3、X2、X4、X5。以X1为基础，顺次加入其他变量逐步回归。首先加入X3回归结果为：Dependent Variable: YMethod: Least SquaresDate: 05/12/03 Time: 14:05Sample: 1974 1987Included observations: 14VariableCoef

14、ficientStd. Errort-StatisticProb.C-39.7947925.01570-1.5907930.1400X10.2115430.0453024.6695810.0007X31.9092460.7241532.6365230.0231R-squared0.953945Mean dependent var142.7129Adjusted R-squared0.945571S.D. dependent var26.09805S.E. of regression6.088671Akaike info criterion6.638146Sum squared resid407

15、.7910Schwarz criterion6.775087Log likelihood-43.46702F-statistic113.9220Durbin-Watson stat1.655554Prob(F-statistic)0.000000 t (-1.590793) (4.669581) （2.636523） R2=0.953945当=5%时，X3参数的t检验显著，不予剔除，加入X2回归得：Dependent Variable: YMethod: Least SquaresDate: 05/12/03 Time: 14:10Sample: 1974 1987Included obser

16、vations: 14VariableCoefficientStd. Errort-StatisticProb.C-34.6287927.82151-1.2446770.2416X10.2063280.0480164.2970540.0016X31.4486691.1753251.2325690.2459X20.0096050.0188750.5088970.6219R-squared0.955107Mean dependent var142.7129Adjusted R-squared0.941640S.D. dependent var26.09805S.E. of regression6.

17、304735Akaike info criterion6.755435Sum squared resid397.4968Schwarz criterion6.938023Log likelihood-43.28805F-statistic70.91803Durbin-Watson stat1.682728Prob(F-statistic)0.000000 t (4.297054) (1.232569) (0.508897) R2=0.955107当=5%时，X3、X2参数的t检验均不显著，但单独对x1、x2进行回归得：Dependent Variable: YMethod: Least Squ

18、aresDate: 05/13/13 Time: 23:05Sample: 1974 1987Included observations: 14VariableCoefficientStd. Errort-StatisticProb.C-40.7838528.00819-1.461400.1733X10.2291490.0453375.0543810.0004X20.0275200.0123232.2332290.0473R-squared0.948287Mean dependent var142.7129Adjusted R-squared0.938885S.D. dependent var

19、26.09805S.E. of regression6.451819Akaike info criterion6.754010Sum squared resid457.8856Schwarz criterion6.890951Log likelihood-44.27807Hannan-Quinn criter.6.741334F-statistic100.8568Durbin-Watson stat1.803344Prob(F-statistic)0.000000 t (5.054381) （2.233229） R2=0.948287 =0.938885因为=0.9388850.941640，

20、X2参数不显著，需要剔除x2保留X1和X3并加入X4回归得：Dependent Variable: YMethod: Least SquaresDate: 05/12/0 Time: 14:16Sample: 1974 1987Included observations: 14VariableCoefficientStd. Errort-StatisticProb.C-37.9988428.00654-1.3567850.2047X10.2103140.0479194.3889780.0014X31.7457671.1785901.4812340.1694X40.2347891.2958740

21、.1811820.8598R-squared0.954096Mean dependent var142.7129Adjusted R-squared0.940324S.D. dependent var26.09805S.E. of regression6.375396Akaike info criterion6.777726Sum squared resid406.4568Schwarz criterion6.960314Log likelihood-43.44408F-statistic69.28123Durbin-Watson stat1.673512Prob(F-statistic)0.

22、000001t (4.388978) (1.481234) (0.181182) R2=0.954096当=5%时，X3和X4参数的t检验不显著，但单独对X1和X4做回归得Dependent Variable: YMethod: Least SquaresDate: 05/13/13 Time: 23:25Sample: 1974 1987Included observations: 14VariableCoefficientStd. Errort-StatisticProb.C-46.1079228.91860-1.5944040.1392X10.2425030.0449665.393027

23、0.0002X41.7042870.8777941.9415570.0782R-squared0.944024Mean dependent var142.7129Adjusted R-squared0.933846S.D. dependent var26.09805S.E. of regression6.712509Akaike info criterion6.833232Sum squared resid495.6355Schwarz criterion6.970173Log likelihood-44.83262Hannan-Quinn criter.6.820556F-statistic

24、92.75612Durbin-Watson stat1.915578Prob(F-statistic)0.000000 t (5.393027) （1.941557） R2=0.944024 =0.933846因为=0.9338460.940324，X4参数不显著，需要剔除x4加入X5回归得：Dependent Variable: YMethod: Lest SquaresDate: 05/12/03 Time: 14:19Sample: 1974 1987Included observations: 14VariableCoefficientStd. Errort-StatisticProb

25、.C-40.8233326.65152-1.5317450.1566X10.2105270.0476684.4165360.0013X32.1447981.3704411.5650420.1486X5-0.1574380.763156-0.2062980.8407R-squared0.954140Mean dependent var142.7129Adjusted R-squared0.940382S.D. dependent var26.09805S.E. of regression6.372306Akaike info criterion6.776756Sum squared resid406.0629Schwarz criterion6.959344Log likelihood-43.43729F-statistic69.35167Durbin-Watson stat1.634831Prob(F-statistic)0.000001