[程序分享] 用stata如何检验panel的自相关和异方差 [推广有奖]

91楼

zhangtao 发表于 2011-4-8 13:58:18

非常非常好好好

92楼

何志伟

发表于 2011-4-9 18:45:49

受益匪浅~~~~~~~~~~~~~~~

93楼

yichikawakiko 发表于 2011-4-12 11:02:39

受益匪浅~~~

94楼

邓艳平 发表于 2011-4-13 23:21:06

thanks 1# nkzhh

95楼

yangyu1107 发表于 2011-4-14 08:31:54

我觉得上面的数据可以不做序列相关检验！

96楼

宋军发 发表于 2011-4-14 10:02:31

正在做面板的，目前还不知道要不要处理自相关和异方差

勤奋努力在前，顺其自然在后！

97楼

luoxi 发表于 2011-4-28 15:22:04

. xtgls lnc lncyjg lnnyqd lnnyjg, panel(heteroskedastic) igls
Iteration 1: tolerance = .01581804
Iteration 2: tolerance = .01178879
Iteration 3: tolerance = .00786204
Iteration 4: tolerance = .00755356
Iteration 5: tolerance = .00757862
Iteration 6: tolerance = .00742984
Iteration 7: tolerance = .00878119
Iteration 8: tolerance = .0085893
Iteration 9: tolerance = .00714584
Iteration 10: tolerance = .00491389
Iteration 11: tolerance = .00284052
Iteration 12: tolerance = .00154705
Iteration 13: tolerance = .00083718
Iteration 14: tolerance = .00045234
Iteration 15: tolerance = .00024345
Iteration 16: tolerance = .00013044
Iteration 17: tolerance = .00006964
Iteration 18: tolerance = .00003708
Iteration 19: tolerance = .00001972
Iteration 20: tolerance = .00001048
Iteration 21: tolerance = 5.568e-06
Iteration 22: tolerance = 2.959e-06
Iteration 23: tolerance = 1.573e-06
Iteration 24: tolerance = 8.363e-07
Iteration 25: tolerance = 4.448e-07
Iteration 26: tolerance = 2.366e-07
Iteration 27: tolerance = 1.259e-07
Iteration 28: tolerance = 6.703e-08

Cross-sectional time-series FGLS regression

Coefficients:  generalized least squares
Panels:       heteroskedastic
Correlation: no autocorrelation

Estimated covariances    =       28       Number of obs    =    364
Estimated autocorrelations =       0       Number of groups =       28
Estimated coefficients    =       4       Time periods    =       13
                                             Wald chi2(3)    =  94950.33
Log likelihood          =  292.3762       Prob > chi2       = 0.0000

------------------------------------------------------------------------------
      lnc |    Coef. Std. Err.    z P>|z|    [95% Conf. Interval]
-------------+----------------------------------------------------------------
   lncyjg | .0442005 .0058945    7.50 0.000    .0326475 .0557536
   lnnyqd | .9746002 .0040893 238.33 0.000    .9665853 .9826152
   lnnyjg | .6989617 .0068999 101.30 0.000    .6854381 .7124852
   _cons | 1.100894 .0226045 48.70 0.000    1.05659 1.145198
------------------------------------------------------------------------------

. estimates store igls

. xtgls lnc lncyjg lnnyqd lnnyjg

Cross-sectional time-series FGLS regression

Coefficients:  generalized least squares
Panels:       homoskedastic
Correlation: no autocorrelation

Estimated covariances    =       1       Number of obs    =    364
Estimated autocorrelations =       0       Number of groups =       28
Estimated coefficients    =       4       Time periods    =       13
                                             Wald chi2(3)    =  13366.27
Log likelihood          =  58.77705       Prob > chi2       = 0.0000

------------------------------------------------------------------------------
      lnc |    Coef. Std. Err.    z P>|z|    [95% Conf. Interval]
-------------+----------------------------------------------------------------
   lncyjg | .0892351 .0141136    6.32 0.000    .061573 .1168973
   lnnyqd | 1.026689 .0099941 102.73 0.000    1.007101 1.046277
   lnnyjg | .5551315 .0178899 31.03 0.000    .5200679 .5901952
   _cons | 1.080897 .0618448 17.48 0.000    .9596838 1.202111
------------------------------------------------------------------------------

. local df=e(N_g)-1

. lrtest igls . , df(`df')

Likelihood-ratio test                               LR chi2(27) = 467.20
(Assumption: . nested in igls)                      Prob > chi2 = 0.0000

.
以上是我按照1楼得说法做的
请高手帮我看下这是什么意思啊
不懂程序滴人啊

98楼

sophiafinn 发表于 2011-5-3 03:05:16

from help file，除此对于small T, large N , 还可以用 xtabond and xtabond2
help ivreg2

help  xtabond2

(Heteroskedastic and autocorrelation-consistent (HAC) inference in an OLS
regression)

      . ivreg2 cinf unem, bw(3) kernel(bartlett) robust small

      . newey cinf unem, lag(2)

(AC and HAC in IV and GMM estimation)

      . ivreg2 cinf (unem = l(1/3).unem), bw(3)

      . ivreg2 cinf (unem = l(1/3).unem), bw(3) gmm2s kernel(thann)

      . ivreg2 cinf (unem = l(1/3).unem), bw(3) gmm2s kernel(qs) robust
         orthog(l1.unem)

(Examples using Large N, Small T Panel Data)

      . use http://fmwww.bc.edu/ec-p/data/macro/abdata.dta

      . tsset id year

(Autocorrelation-consistent inference in an IV regression)

      . ivreg2 n (w k ys = d.w d.k d.ys d2.w d2.k d2.ys), bw(1) kernel(tru)

(Two-step effic. GMM in the presence of arbitrary heteroskedasticity and
autocorrelation)

      . ivreg2 n (w k ys = d.w d.k d.ys d2.w d2.k d2.ys), bw(2) gmm2s
         kernel(tru) robust

(Two-step effic. GMM in the presence of arbitrary heterosked. and
intragroup correlation)

      . ivreg2 n (w k ys = d.w d.k d.ys d2.w d2.k d2.ys), gmm2s cluster(id)