用VAR里面的residual test,有autocorrelation LM test, 怀特检验和normality test,不明白结果如何分别判断出是不是会有自相关,异方差和normality。结果如下:
Sample: 1985Q1 2010Q3
Included observations: 99
Lags LM-Stat Prob
1 12.55659 0.7049
2 13.48256 0.6372
3 19.88127 0.2256
Probs from chi-square with 16 df.
VEC Residual Heteroskedasticity Tests: Includes Cross Terms
Date: 09/03/11 Time: 18:50
Sample: 1985Q1 2010Q3
Included observations: 99
Joint test:
Chi-sq df Prob.
637.0631 540 0.0025
Individual components:
Dependent R-squared F(54,44) Prob. Chi-sq(54) Prob.
res1*res1 0.644706 1.478541 0.0915 63.82592 0.1693
res2*res2 0.523146 0.893916 0.6550 51.79148 0.5600
res3*res3 0.482004 0.758198 0.8344 47.71838 0.7139
res4*res4 0.818837 3.682883 0.0000 81.06490 0.0100
res2*res1 0.784525 2.966673 0.0002 77.66801 0.0191
res3*res1 0.659504 1.578206 0.0603 65.29086 0.1396
res3*res2 0.615377 1.303664 0.1832 60.92237 0.2410
res4*res1 0.836105 4.156752 0.0000 82.77440 0.0071
res4*res2 0.807198 3.411349 0.0000 79.91256 0.0125
res4*res3 0.727588 2.176300 0.0044 72.03124 0.0510
VEC Residual Normality Tests
Orthogonalization: Residual Correlation (Doornik-Hansen)
Null Hypothesis: residuals are multivariate normal
Date: 09/03/11 Time: 18:52
Sample: 1985Q1 2010Q3
Included observations: 99
Component Skewness Chi-sq df Prob.
1 0.159301 0.470085 1 0.4929
2 0.033848 0.021429 1 0.8836
3 -0.431809 3.253826 1 0.0713
4 -0.855223 10.87639 1 0.0010
Joint 14.62173 4 0.0056
Component Kurtosis Chi-sq df Prob.
1 2.170197 3.714644 1 0.0539
2 2.068698 4.433743 1 0.0352
3 2.536771 3.371966 1 0.0663
4 4.259721 0.282659 1 0.5950
Joint 11.80301 4 0.0189
Component Jarque-Bera df Prob.
1 4.184729 2 0.1234
2 4.455172 2 0.1078
3 6.625792 2 0.0364
4 11.15905 2 0.0038
Joint 26.42474 8 0.0009
拜托拜托,论文真的是急用,不知道有木有高手可以指导一下~~