用面板数据的固定效果模型做出来的结果所有相关都为0。是本身这个模型不适合固定效果还是数据输入问题。求大神赐教。模型,结果如下,还有数据整理格式如下。ps:截面数据的普通回归是可以做出结果,不知道为什么放到面板数据就不行了。。
. xtreg C D E F G H I J K L,fe
Fixed-effects (within) regression Number of obs = 596
Group variable: com Number of groups = 120
R-sq: Obs per group:
within = . min = 1
between = . avg = 5.0
overall = . max = 5
F(9,467) = .
corr(u_i, Xb) = . Prob > F = .
------------------------------------------------------------------------------
C | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
D | -6.29e-34 .067369 -0.00 1.000 -.132384 .132384
E | 3.50e-34 .0092483 0.00 1.000 -.0181735 .0181735
F | 4.31e-34 .0160188 0.00 1.000 -.0314778 .0314778
G | -1.96e-32 .5285 -0.00 1.000 -1.038533 1.038533
H | -1.90e-31 .3700038 -0.00 1.000 -.7270785 .7270785
I | -6.60e-34 .1512533 -0.00 1.000 -.2972213 .2972213
J | 1.67e-31 4.016859 0.00 1.000 -7.893356 7.893356
K | -5.57e-32 2.435495 -0.00 1.000 -4.785885 4.785885
L | 1.17e-32 .4278345 0.00 1.000 -.840719 .840719
_cons | .0757556 3.929368 0.02 0.985 -7.645675 7.797187
-------------+----------------------------------------------------------------
sigma_u | .56524716
sigma_e | .63801504
rho | .43974487 (fraction of variance due to u_i)
------------------------------------------------------------------------------
F test that all u_i=0: F(119, 467) = -1.36 Prob > F = 1.0000
最下面是整理的数据格式
A B C D E F G H I J K L
1 2013 .5863828 0 19.5883 7.0569244 .28941632 12.017573 .12814846 .02031364 .01089852 .10934112
1 2014 .5863828 0 19.58918 5.4793043 .2927886 11.889833 .12192658 .02634167 .00643258 .13796155
1 2015 .5863828 1 19.58954 4.3292617 .29723801 11.819897 .09752003 .02074847 .0120641 .17014052
1 2016 .5863828 1 19.58989 4.1042645 .30531928 11.849431 .15154043 .01908754 .00653209 .19021834
1 2017 .5863828 1 19.59042 3.6205729 .28745888 11.862682 .02433351 .01308449 .00461592 .16520083
2 2013 .2949838 1 12.34909 2.1585847 .26851686 10.469232 .10065459 .00009505 .00815997 .20072971
2 2014 .2949838 1 12.35214 1.9275204 .26759647 10.454972 .18972878 .00009434 .01244256 .17686737
2 2015 .2949838 1 12.35557 2.1100705 .3375844 10.621038 .08173634 .0000549 .01152561 .15431233
2 2016 .2949838 1 12.35938 1.7735301 .37691721 10.497878 .11712151 .00003263 .01119073 .11469295
2 2017 .2949838 1 12.36243 1.67534 .336272 10.541102 .07893268 .00003526 .01175468 .10802256
3 2013 -.1213902 1 .24675 1.0377446 .45402564 9.1911372 .01992607 .02079242 -.00358092 .02787615
3 2014 -.1213902 1 .27273 1.0378331 .47295963 9.2074863 .00663668 .01864853 -.00729487 .0305179
3 2015 -.1213902 0 .28571 1.0710785 .45983172 9.2868754 .08371295 .01779302 -.00659199 .0309639
3 2016 -.1213902 0 .31169 1.1311249 .45335992 9.3477517 .13784556 .01821415 .03491944 .04532197
3 2017 -.1213902 0 .68831 1.0830189 .44548458 9.3923786 .05546656 .01752103 .00045054 .0568589
4 2013 -.0337236 1 2.99052 1.5297637 .35580452 10.839776 .00629708 .08246088 -.00745606 .14001881
4 2014 -.0337236 1 2.99052 1.1565086 .33796251 10.576753 -.02851626 .08082857 -.01771051 .07932208
4 2015 -.0337236 1 2.99052 1.2032425 .37932907 10.692195 .05779444 .0682979 -.00449663 .05215402
4 2016 -.0337236 1 2.99052 1.104018 .37435193 10.589147 .00535857 .07261909 .01482306 .03400443
4 2017 -.0337236 0 2.99052 1.0535565 .40248919 10.636506 .08033149 .07515954 -.01946284 .06955783