计量经济大作业(4)

S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic)

830.0411 Akaike info criterion 3444841. Schwarz criterion -70.61868 Hannan-Quinn criter. 1800.478 Durbin-Watson stat 0.000000

16.58193 16.66959 16.39277 1.999883

LS(W=W2) Y C X1 X2 X3

????Dependent Variable: Y Method: Least Squares Date: 12/21/12 Time: 19:44 Sample: 13 21

Included observations: 9 Weighting series: W2

Variable C X1 X2 X3

R-squared

Coefficient -32944.61 0.131223 0.276088 279.3597

Std. Error 18525.88 0.034694 0.134632 195.2762

t-Statistic -1.778302 3.782248 2.050693 1.430588

Prob. 0.1355 0.0129 0.0956 0.2120

42578.71 10509.73 16.55059 16.63825 16.36143 1.996676

47786.93 23582.57 4353624.

Weighted Statistics

0.999035 Mean dependent var 0.998456 S.D. dependent var 817.1371 Akaike info criterion 3338565. Schwarz criterion -70.47767 Hannan-Quinn criter. 1725.117 Durbin-Watson stat 0.000000

Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic)

R-squared Adjusted R-squared S.E. of regression Durbin-Watson stat

Unweighted Statistics

0.999021 Mean dependent var 0.998434 S.D. dependent var 933.1263 Sum squared resid 1.748834

LS(W=W3) Y C X1 X2 X3

????Dependent Variable: Y Method: Least Squares

Date: 12/21/12 Time: 19:45 Sample: 13 21

Included observations: 9 Weighting series: W3

Variable C X1 X2 X3

R-squared

Coefficient -44033.06 0.117363 0.329795 395.1662

Std. Error 18642.29 0.039025 0.147510 199.3679

t-Statistic -2.361999 3.007397 2.235741 1.982095

Prob. 0.0646 0.0298 0.0756 0.1043

47675.04 23275.24 16.71963 16.80729 16.53047 2.045831

47786.93 23582.57 4004549.

Weighted Statistics

0.999107 Mean dependent var 0.998571 S.D. dependent var 889.2037 Akaike info criterion 3953416. Schwarz criterion -71.23834 Hannan-Quinn criter. 1864.539 Durbin-Watson stat 0.000000

Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic)

R-squared Adjusted R-squared S.E. of regression Durbin-Watson stat

Unweighted Statistics

0.999100 Mean dependent var 0.998560 S.D. dependent var 894.9356 Sum squared resid 2.057822

LS(W=W4) Y C X1 X2 X3

????Dependent Variable: Y Method: Least Squares Date: 12/21/12 Time: 19:46 Sample: 13 21

Included observations: 9 Weighting series: W4

Variable C X1 X2 X3

Coefficient

-41293.70 0.120675 0.310310 370.7943

Std. Error

19862.84 0.037543 0.142104 213.0039

t-Statistic

-2.078942 3.214344 2.183688 1.740786

Prob.

0.0922 0.0236 0.0807 0.1422

R-squared

Weighted Statistics

46721.17 22102.73 16.76827 16.85592 16.57911 2.127479 47786.93 23582.57 4437614.

0.998900 Mean dependent var 0.998240 S.D. dependent var 911.0924 Akaike info criterion 4150447. Schwarz criterion -71.45721 Hannan-Quinn criter. 1513.526 Durbin-Watson stat 0.000000

Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic)

R-squared Adjusted R-squared S.E. of regression Durbin-Watson stat

Unweighted Statistics

0.999003 Mean dependent var 0.998404 S.D. dependent var 942.0843 Sum squared resid 1.856697

LS(W=W5) Y C X1 X2 X3

????Dependent Variable: Y Method: Least Squares Date: 12/21/12 Time: 19:46 Sample: 13 21

Included observations: 9 Weighting series: W5

Variable C X1 X2 X3

R-squared

Coefficient -39204.90 0.126106 0.283912 350.0404

Std. Error 19240.41 0.033338 0.127855 205.5793

t-Statistic -2.037633 3.782712 2.220580 1.702702

Prob. 0.0972 0.0129 0.0771 0.1494

44488.70 24145.09 16.61973 16.70739 16.43057 2.360020

Weighted Statistics

0.998886 Mean dependent var 0.998218 S.D. dependent var 845.8792 Akaike info criterion 3577558. Schwarz criterion -70.78880 Hannan-Quinn criter. 1495.034 Durbin-Watson stat 0.000000

Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic)

R-squared

Unweighted Statistics

47786.93 23582.57 5339686.

0.998800 Mean dependent var 0.998080 S.D. dependent var 1033.411 Sum squared resid 1.569813

Adjusted R-squared S.E. of regression Durbin-Watson stat

经估计检验发现用权数W3的效果最好。下面仅给出用权数W3的结果。

③ 对所估计的模型再进行White检验，观察异方差的调整情况

对所估计的模型再进行White检验，其结果对应图所示。所对应的White检验显示，P值较大，所以接受不存在异方差的原假设，即认为已经消除了回归模型的异方差性。

2. 自相关检验 （1）图示法

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