Eviews多重共线性检验及补救
关键词:eviews多重共线性、eviews多重共线性操作、多重共线性检验、eviews判断多重共线性
目的:1、正确使用EVIEWS
2、能根据计算结果进行多重共线性检验和出现多重共线性时的补救。
3、数据为demo data2
实例:我国钢材供应量分析(多重共线性检验及补救)
通过分析我国改革开放以来(1978-1997)钢材供应量的历史资料,可以建立一个单一方程模型。根据理论及对现实情况的认识,影响我国钢材供应量Y(万吨)的主要因素有:原油产量X1(万吨),生铁产量X2(万吨),原煤产量X3(万吨),电力产量X4(亿千瓦小时),固定资产投资X5(亿元),国内生产总值X6(亿元),铁路运输量X7(万吨)。
设模型的函数形式为:
一、运用OLS估计法对上式中参数进行估计,EVIEWS操作步骤为:
1、 在FILE菜单中选择NEW-WORKFILE,输入起止时间。
2、 在主窗口菜单选QUICK-EMPTY GROUP,在编辑数据区输入Y X1 X2 X3 X4 X5 X6 X7所对应的数据。
3、 在主窗口菜单选在QUICK-ESTIMATE EQUATION,对参数做OSL估计,输出结果见下表:
Variable |
Coefficient |
Std. Error |
t-Statistic |
Prob. |
C |
139.2362 |
718.2493 |
0.193855 |
0.8495 |
X1 |
-0.051954 |
0.090753 |
-0.572483 |
0.5776 |
X2 |
0.127532 |
0.132466 |
0.962751 |
0.3547 |
X3 |
-24.29427 |
97.48792 |
-0.249203 |
0.8074 |
X4 |
0.863283 |
0.186798 |
4.621475 |
0.0006 |
X5 |
0.330914 |
0.105592 |
3.133889 |
0.0086 |
X6 |
-0.070015 |
0.025490 |
-2.746755 |
0.0177 |
X7 |
0.002305 |
0.019087 |
0.120780 |
0.9059 |
R-squared |
0.999222 |
Mean dependent var |
5153.350 |
|
Adjusted R-squared |
0.998768 |
S.D. dependent var |
2511.950 |
|
S.E. of regression |
88.17626 |
Akaike info criterion |
12.08573 |
|
Sum squared resid |
93300.63 |
Schwarz criterion |
12.48402 |
|
Log likelihood |
-112.8573 |
F-statistic |
2201.081 |
|
Durbin-Watson stat |
1.703427 |
Prob(F-statistic) |
0.000000 |
Y = 139.2361608 – 0.05195439459*X1 + 0.1275320853*X2 – 24.294272*X3 + 0.8632825292*X4 + 0.330913843*X5 – 0.07001518918*X6 + 0.002305379405*X7
二、分析
由F=2201.081>F0.05(7,12)=2.91(显著性水平a=0.05),表明模型从整体上看钢材供应量与解释变量之间线性关系显著。
三、检验
计算解释变量之间的简单相关系数。EVIEWS过程如下:
1、主菜单QUICK-GROUP STATISTICS-CORRRELATION,在对话框中输入X1 X2 X3 X4 X5 X6 X7,结果如下:
X1 |
X2 |
X3 |
X4 |
X5 |
X6 |
X7 |
|
X1 |
1.000000 |
0.921956 |
0.975474 |
0.931882 |
0.826401 |
0.845837 |
0.986815 |
X2 |
0.921956 |
1.000000 |
0.964400 |
0.994921 |
0.969686 |
0.972530 |
0.931689 |
X3 |
0.975474 |
0.964400 |
1.000000 |
0.974809 |
0.894963 |
0.913344 |
0.982943 |
X4 |
0.931882 |
0.994921 |
0.974809 |
1.000000 |
0.959613 |
0.969105 |
0.945444 |
X5 |
0.826401 |
0.969686 |
0.894963 |
0.959613 |
1.000000 |
0.996169 |
0.827643 |
X6 |
0.845837 |
0.972530 |
0.913344 |
0.969105 |
0.996169 |
1.000000 |
0.846079 |
X7 |
0.986815 |
0.931689 |
0.982943 |
0.945444 |
0.827643 |
0.846079 |
1.000000 |
2、由上表可以看出,解释变量之间存在高度线性相关性。尽管方程整体线性回归拟合较好,但X1 X2 X3 X7变量的参数t值并不显著, X3 X6 系数的符号与经济意义相悖。表明模型确实存在严重的多重共线性。
四、修正
1、运用OLS方法逐一求Y对各个解释变量的回归。结合经济意义和统计检验选出拟合效果最好的一元线性回归方程。
Variable |
Coefficient |
Std. Error |
t-Statistic |
Prob. |
|
C |
-10123.78 |
1528.060 |
-6.625250 |
0.0000 |
|
X1 |
1.181784 |
0.116936 |
10.10629 |
0.0000 |
|
R-squared |
0.850171 |
Mean dependent var |
5153.350 |
||
Adjusted R-squared |
0.841847 |
S.D. dependent var |
2511.950 |
||
S.E. of regression |
998.9623 |
Akaike info criterion |
16.74595 |
||
Sum squared resid |
17962663 |
Schwarz criterion |
16.84552 |
||
Log likelihood |
-165.4595 |
F-statistic |
102.1371 |
||
Durbin-Watson stat |
0.217842 |
Prob(F-statistic) |
0.000000 |
Variable |
Coefficient |
Std. Error |
t-Statistic |
Prob. |
C |
-618.7199 |
108.3930 |
-5.708116 |
0.0000 |
X2 |
0.926212 |
0.016019 |
57.82017 |
0.0000 |
R-squared |
0.994645 |
Mean dependent var |
5153.350 |
|
Adjusted R-squared |
0.994347 |
S.D. dependent var |
2511.950 |
|
S.E. of regression |
188.8610 |
Akaike info criterion |
13.41454 |
|
Sum squared resid |
642032.9 |
Schwarz criterion |
13.51411 |
|
Log likelihood |
-132.1454 |
F-statistic |
3343.172 |
|
Durbin-Watson stat |
0.962290 |
Prob(F-statistic) |
0.000000 |
Variable |
Coefficient |
Std. Error |
t-Statistic |
Prob. |
C |
-3770.942 |
581.6642 |
-6.483023 |
0.0000 |
X3 |
926.7178 |
58.38537 |
15.87243 |
0.0000 |
R-squared |
0.933317 |
Mean dependent var |
5153.350 |
|
Adjusted R-squared |
0.929612 |
S.D. dependent var |
2511.950 |
|
S.E. of regression |
666.4367 |
Akaike info criterion |
15.93641 |
|
Sum squared resid |
7994483. |
Schwarz criterion |
16.03598 |
|
Log likelihood |
-157.3641 |
F-statistic |
251.9341 |
|
Durbin-Watson stat |
0.477559 |
Prob(F-statistic) |
0.000000 |
Variable |
Coefficient |
Std. Error |
t-Statistic |
Prob. |
|
C |
-34.32474 |
91.75324 |
-0.374098 |
0.7127 |
|
X4 |
0.884047 |
0.014146 |
62.49381 |
0.0000 |
|
R-squared |
0.995412 |
Mean dependent var |
5153.350 |
||
Adjusted R-squared |
0.995157 |
S.D. dependent var |
2511.950 |
||
S.E. of regression |
174.8044 |
Akaike info criterion |
13.25985 |
||
Sum squared resid |
550018.2 |
Schwarz criterion |
13.35942 |
||
Log likelihood |
-130.5985 |
F-statistic |
3905.476 |
||
Durbin-Watson stat |
0.824221 |
Prob(F-statistic) |
0.000000 |
||
Variable |
Coefficient |
Std. Error |
t-Statistic |
Prob. |
|
C |
2896.350 |
211.0245 |
13.72518 |
0.0000 |
|
X5 |
0.572451 |
0.036983 |
15.47892 |
0.0000 |
|
R-squared |
0.930123 |
Mean dependent var |
5153.350 |
||
Adjusted R-squared |
0.926241 |
S.D. dependent var |
2511.950 |
||
S.E. of regression |
682.2088 |
Akaike info criterion |
15.98319 |
||
Sum squared resid |
8377359. |
Schwarz criterion |
16.08276 |
||
Log likelihood |
-157.8319 |
F-statistic |
239.5971 |
||
Durbin-Watson stat |
0.181794 |
Prob(F-statistic) |
0.000000 |
Variable |
Coefficient |
Std. Error |
t-Statistic |
Prob. |
C |
2720.664 |
205.3405 |
13.24952 |
0.0000 |
X6 |
0.108665 |
0.006568 |
16.54535 |
0.0000 |
R-squared |
0.938303 |
Mean dependent var |
5153.350 |
|
Adjusted R-squared |
0.934875 |
S.D. dependent var |
2511.950 |
|
S.E. of regression |
641.0376 |
Akaike info criterion |
15.85869 |
|
Sum squared resid |
7396725. |
Schwarz criterion |
15.95827 |
|
Log likelihood |
-156.5869 |
F-statistic |
273.7485 |
|
Durbin-Watson stat |
0.259927 |
Prob(F-statistic) |
0.000000 |
Variable |
Coefficient |
Std. Error |
t-Statistic |
Prob. |
|
C |
-9760.099 |
1317.227 |
-7.409582 |
0.0000 |
|
X7 |
0.106826 |
0.009326 |
11.45524 |
0.0000 |
|
R-squared |
0.879375 |
Mean dependent var |
5153.350 |
||
Adjusted R-squared |
0.872673 |
S.D. dependent var |
2511.950 |
||
S.E. of regression |
896.3356 |
Akaike info criterion |
16.52915 |
||
Sum squared resid |
14461517 |
Schwarz criterion |
16.62872 |
||
Log likelihood |
-163.2915 |
F-statistic |
131.2225 |
||
Durbin-Watson stat |
0.183657 |
Prob(F-statistic) |
0.000000 |
经分析在7个一元回归模型中钢材供应量Y对电力产量X4的线性关系强,拟合度好,即:
Y = -34.32474492 + 0.8840472792*X4
(-0.374098) (62.49381)
R2= 0.995412 S.E.=174.8044,F=3905.476
截距项不显著,去掉,重新估计:
Y = 0.8792594492*X4
2、逐步回归。
将其余解释变量逐一代入上式,得如下模型:
Y = -0.005935225118*X1 + 0.8906555628*X4
(-0.604681) (45.03888)
R2= 0.995469 S.E.=173.7270, F=3954.290
式中X1不显著,删去,继续:
Y = 0.1741981867*X2 + 0.6978252624*X4
(1.879546) (7.217200)
R2= 0.996135 S.E.=160.4431, F=4639.290
Y = 0.2753793175*X2 + 0.5595511241*X4 + 0.04060861466*X5
(3.082485) (5.637333) (2.615818)
R2= 0.997244 S.E.=139.4060, F=3075.985
Y = 0.466836912*X2 + 0.5219953469*X4 – 0.03080496295*X5 – 0.004998894793*X7
(3.245804) (5.366654) (-0.674009) (-1.651391)
R2= 0.997646 S.E.=132.8222, F=2259.899
X7不符合经济意义,应去掉。
所以:
Y = 0.2753793175*X2 + 0.5595511241*X4 + 0.04060861466*X5
(3.082485) (5.637333) (2.615818)
R2= 0.997244 S.E.=139.4060, F=3075.985
即为最优模型。
Dependent Variable: Y |
||||
Method: Least Squares |
||||
Date: 10/17/05 Time: 22:53 |
||||
Sample: 1978 1997 |
||||
Included observations: 20 |
||||
Variable |
Coefficient |
Std. Error |
t-Statistic |
Prob. |
X2 |
0.275379 |
0.089337 |
3.082485 |
0.0068 |
X4 |
0.559551 |
0.099258 |
5.637333 |
0.0000 |
X5 |
0.040609 |
0.015524 |
2.615818 |
0.0181 |
R-squared |
0.997244 |
Mean dependent var |
5153.350 |
|
Adjusted R-squared |
0.996920 |
S.D. dependent var |
2511.950 |
|
S.E. of regression |
139.4060 |
Akaike info criterion |
12.85014 |
|
Sum squared resid |
330378.5 |
Schwarz criterion |
12.99950 |
|
Log likelihood |
-125.5014 |
F-statistic |
3075.985 |
|
Durbin-Watson stat |
0.790639 |
Prob(F-statistic) |
0.000000 |
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