发帖
雷达卡
heckman回归结果结果1如下，其中最后一行LR test of indep. eqns. (rho = 0)是检验选择方程和结果方程的两误差项相关系数时，P值为0好，还是越大越好？其中这个相关系数是结果方程里面加了lambda 还是没有加的时候的？
同理：switch回归结果结果2的LR test of indep. eqns. Prob > chi2 = 0.4625，P值为0好，还是越大越好？其中这个相关系数是结果方程里面加了lambda 还是没有加的时候的？
还有treatreg结果3里的LR test of indep. eqns. (rho = 0): Prob > chi2 = 0.3060，P值为0好，还是越大越好？其中这个相关系数是结果方程里面加了lambda 还是没有加的时候的？
结果1：
Heckman selection model Number of obs = 4103
(regression model with sample selection) Censored obs = 2842
Uncensored obs = 1261
Wald chi2(8) = 302.15
Log likelihood = -3075.558 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
lnwage1 | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
lnwage1 |
educ1 | .1395881 .0178205 7.83 0.000 .1046606 .1745157
gznx | .0680667 .0057062 11.93 0.000 .0568828 .0792506
gznx2 | -.0013655 .0001384 -9.87 0.000 -.0016367 -.0010942
sex1 | -.1818685 .0349924 -5.20 0.000 -.2504523 -.1132846
diqu1 | -.0101119 .0017985 -5.62 0.000 -.0136369 -.006587
mingzhu1 | .1307868 .2397918 0.55 0.585 -.3391965 .6007701
hangye1 | -.003255 .0044732 -0.73 0.467 -.0120223 .0055122
zhiye1 | -.0587102 .0094012 -6.24 0.000 -.0771362 -.0402842
_cons | 9.154971 .191145 47.90 0.000 8.780334 9.529608
-------------+----------------------------------------------------------------
select |
age | .0029494 .0057781 0.51 0.610 -.0083754 .0142742
married1 | -.1511669 .0804059 -1.88 0.060 -.3087595 .0064258
educ1 | .2756489 .018804 14.66 0.000 .2387938 .312504
gznx | .0356294 .009319 3.82 0.000 .0173644 .0538944
gznx2 | -.0005013 .0002228 -2.25 0.024 -.000938 -.0000645
sex1 | -.3002719 .0481841 -6.23 0.000 -.3947111 -.2058328
diqu1 | .0083817 .0027029 3.10 0.002 .0030842 .0136792
mingzhu1 | -.1432343 .3986386 -0.36 0.719 -.9245517 .638083
hangye1 | .0698959 .0051068 13.69 0.000 .0598869 .079905
zhiye1 | -.0600539 .0121095 -4.96 0.000 -.0837881 -.0363197
_cons | -2.447347 .2210819 -11.07 0.000 -2.88066 -2.014035
-------------+----------------------------------------------------------------
/athrho | .0567385 .1288986 0.44 0.660 -.195898 .3093751
/lnsigma | -.629835 .020482 -30.75 0.000 -.669979 -.5896909
-------------+----------------------------------------------------------------
rho | .0566777 .1284845 -.1934299 .2998685
sigma | .5326797 .0109104 .5117193 .5544986
lambda | .0301911 .0685886 -.1042401 .1646223
------------------------------------------------------------------------------
LR test of indep. eqns. (rho = 0): chi2(1) = 0.19 Prob > chi2 = 0.6671
结果2：
Endogenous switching regression model Number of obs = 4103
Wald chi2(8) = 301.03
Log likelihood = -6571.9785 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
lnwage_1 |
educ1 | .1392999 .0178692 7.80 0.000 .1042768 .1743229
gznx | .0680439 .0057088 11.92 0.000 .0568548 .0792331
gznx2 | -.0013653 .0001384 -9.86 0.000 -.0016366 -.001094
sex1 | -.1815398 .035 -5.19 0.000 -.2501385 -.112941
diqu1 | -.0101217 .0017982 -5.63 0.000 -.0136461 -.0065974
mingzhu1 | .1309258 .2397796 0.55 0.585 -.3390336 .6008852
hangye1 | -.0033266 .004481 -0.74 0.458 -.0121092 .0054561
zhiye1 | -.0586382 .0094082 -6.23 0.000 -.0770781 -.0401984
_cons | 9.158828 .1918539 47.74 0.000 8.782801 9.534855
-------------+----------------------------------------------------------------
lnwage_0 |
educ1 | .1659242 .0156976 10.57 0.000 .1351574 .196691
gznx | .0407454 .0053975 7.55 0.000 .0301665 .0513243
gznx2 | -.0009308 .0001373 -6.78 0.000 -.0011999 -.0006617
sex1 | -.2378676 .0339138 -7.01 0.000 -.3043374 -.1713978
diqu1 | -.0151706 .0019655 -7.72 0.000 -.0190228 -.0113183
mingzhu1 | .1356362 .2771832 0.49 0.625 -.4076329 .6789052
hangye1 | -.0267581 .0044693 -5.99 0.000 -.0355177 -.0179985
zhiye1 | -.0573029 .008416 -6.81 0.000 -.073798 -.0408078
_cons | 9.225561 .0982616 93.89 0.000 9.032971 9.41815
-------------+----------------------------------------------------------------
jiuye |
gznx2 | -.0004953 .0002228 -2.22 0.026 -.000932 -.0000586
diqu1 | .0083044 .0027054 3.07 0.002 .0030019 .0136068
mingzhu1 | -.1432879 .3988394 -0.36 0.719 -.9249987 .6384229
hangye1 | .0698855 .005107 13.68 0.000 .059876 .079895
zhiye1 | -.0602387 .0121132 -4.97 0.000 -.0839801 -.0364973
educ1 | .276282 .0188318 14.67 0.000 .2393724 .3131916
gznx | .0353159 .0093293 3.79 0.000 .0170309 .0536009
sex1 | -.3001865 .0481778 -6.23 0.000 -.3946133 -.2057598
age | .0029598 .005775 0.51 0.608 -.008359 .0142787
married1 | -.14287 .0812414 -1.76 0.079 -.3021003 .0163602
_cons | -2.453753 .2211733 -11.09 0.000 -2.887245 -2.020262
-------------+----------------------------------------------------------------
/lns1 | -.629946 .0204252 -30.84 0.000 -.6699785 -.5899134
/lns2 | -.1880147 .0133963 -14.03 0.000 -.214271 -.1617584
/r1 | .0535836 .1294236 0.41 0.679 -.200082 .3072492
/r2 | -.0530449 .0788697 -0.67 0.501 -.2076266 .1015369
-------------+----------------------------------------------------------------
sigma_1 | .5326206 .0108789 .5117196 .5543753
sigma_2 | .8286025 .0111002 .8071297 .8506467
rho_1 | .0535324 .1290527 -.1974541 .2979325
rho_2 | -.0529952 .0786482 -.2046937 .1011894
------------------------------------------------------------------------------
LR test of indep. eqns. : chi2(1) = 0.54 Prob > chi2 = 0.4625
------------------------------------------------------------------------------
结果3：
Treatment-effects model -- MLE Number of obs = 4103
Wald chi2(9) = 1380.45
Log likelihood = -6740.4587 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
lnwage |
educ1 | .1529527 .0127364 12.01 0.000 .1279899 .1779155
gznx | .0473132 .004194 11.28 0.000 .0390931 .0555333
gznx2 | -.001037 .0001051 -9.86 0.000 -.0012431 -.0008309
sex1 | -.2191972 .0262718 -8.34 0.000 -.270689 -.1677055
diqu1 | -.0135708 .0014606 -9.29 0.000 -.0164335 -.0107082
mingzhu1 | .1587472 .2024493 0.78 0.433 -.2380461 .5555405
hangye1 | -.0210628 .0034741 -6.06 0.000 -.027872 -.0142537
zhiye1 | -.0575607 .0066483 -8.66 0.000 -.0705912 -.0445302
jiuye | .49916 .094636 5.27 0.000 .3136768 .6846432
_cons | 9.135239 .0769067 118.78 0.000 8.984505 9.285974
-------------+----------------------------------------------------------------
jiuye |
age | .0030314 .0057653 0.53 0.599 -.0082685 .0143312
married1 | -.1192037 .0803583 -1.48 0.138 -.276703 .0382956
educ1 | .2768117 .0188202 14.71 0.000 .2399248 .3136987
gznx | .0343857 .0093005 3.70 0.000 .0161571 .0526144
gznx2 | -.0004788 .0002224 -2.15 0.031 -.0009146 -.0000429
sex1 | -.3004198 .0481485 -6.24 0.000 -.3947891 -.2060505
diqu1 | .0082192 .0027058 3.04 0.002 .0029158 .0135225
mingzhu1 | -.1383475 .3987047 -0.35 0.729 -.9197944 .6430993
hangye1 | .069975 .0051069 13.70 0.000 .0599656 .0799844
zhiye1 | -.0602661 .0121108 -4.98 0.000 -.0840028 -.0365294
_cons | -2.471217 .2213025 -11.17 0.000 -2.904961 -2.037472
-------------+----------------------------------------------------------------
/athrho | -.0908895 .0707034 -1.29 0.199 -.2294655 .0476865
/lnsigma | -.2814085 .0115057 -24.46 0.000 -.3039592 -.2588578
-------------+----------------------------------------------------------------
rho | -.09064 .0701225 -.2255211 .0476504
sigma | .75472 .0086836 .737891 .7719328
lambda | -.0684079 .0531503 -.1725806 .0357649
------------------------------------------------------------------------------
LR test of indep. eqns. (rho = 0): chi2(1) = 1.05 Prob > chi2 = 0.3060
提升卡
置顶卡
沉默卡
变色卡
抢沙发
千斤顶
显身卡
京公网安备 11010802022788号
