stata 自相关Breusch-Godfrey LM test for autocorrelation---------------------------------------------------------------------------lags(p) | chi2 df Prob > chi2-------------+-------------------------------------------------------------1 | 0.001 1 0

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stata 自相关Breusch-Godfrey LM test for autocorrelation---------------------------------------------------------------------------lags(p) | chi2 df Prob > chi2-------------+-------------------------------------------------------------1 | 0.001 1 0

stata 自相关Breusch-Godfrey LM test for autocorrelation---------------------------------------------------------------------------lags(p) | chi2 df Prob > chi2-------------+-------------------------------------------------------------1 | 0.001 1 0
stata 自相关
Breusch-Godfrey LM test for autocorrelation
---------------------------------------------------------------------------
lags(p) | chi2 df Prob > chi2
-------------+-------------------------------------------------------------
1 | 0.001 1 0.9738
---------------------------------------------------------------------------
H0:no serial correlation
Breusch-Godfrey LM test for autocorrelation
---------------------------------------------------------------------------
lags(p) | chi2 df Prob > chi2
-------------+-------------------------------------------------------------
2 | 0.079 2 0.9611
---------------------------------------------------------------------------
H0:no serial correlation
说明有自相关吗?

stata 自相关Breusch-Godfrey LM test for autocorrelation---------------------------------------------------------------------------lags(p) | chi2 df Prob > chi2-------------+-------------------------------------------------------------1 | 0.001 1 0
第一个的p值大于卡方边界值的可能性为0.97,则拒绝原假设,存在自相关性;第二个一样,有自相关出现.说明模型在lag(1)和lag(2)存在自相关不管置信区间是90%,95%,99%.