xddlovejiao1314 发表于 2015-10-15 21:56
这个其实就是工具变量的选取,然而工具变量的好坏一般很多都是根据常识来选的。如陈强老师的书上用父母的受 ...
您好,请问像下面这种情况,外生变量很显著,可是处理效应的p值也很显著,这种表示什么?
. treatreg lnincome work_hours homework gy zx dz sx age age2 experience hukou healthy is_party is_urban east west, treat
> (married=sexratio) two first
Probit regression Number of obs = 2012
LR chi2(1) = 23.29
Prob > chi2 = 0.0000
Log likelihood = -1115.3559 Pseudo R2 = 0.0103
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married | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
sexratio | -.6389774 .1329911 -4.80 0.000 -.8996353 -.3783196
_cons | 1.465433 .1669089 8.78 0.000 1.138298 1.792569
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Treatment-effects model -- two-step estimates Number of obs = 2012
Wald chi2(16) = 6566.89
Prob > chi2 = 0.0000
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| Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
lnincome |
work_hours | .1364251 .0019359 70.47 0.000 .1326308 .1402195
homework | -.204924 .0339245 -6.04 0.000 -.2714149 -.1384331
gy | 1.4425 .1638876 8.80 0.000 1.121286 1.763714
zx | -.0240408 .2072328 -0.12 0.908 -.4302096 .3821281
dz | .7353449 .3613597 2.03 0.042 .0270929 1.443597
sx | .8869037 .7498048 1.18 0.237 -.5826868 2.356494
age | .3788979 .0839238 4.51 0.000 .2144102 .5433856
age2 | -.0038905 .0011934 -3.26 0.001 -.0062294 -.0015516
experience | -.0611817 .0308369 -1.98 0.047 -.1216209 -.0007425
hukou | .0758493 .1304975 0.58 0.561 -.1799212 .3316197
healthy | .0001312 .0488811 0.00 0.998 -.0956739 .0959364
is_party | .4020509 .2218493 1.81 0.070 -.0327656 .8368675
is_urban | .1050207 .1106477 0.95 0.343 -.1118448 .3218861
east | .5669382 .1519215 3.73 0.000 .2691775 .8646989
west | .0782284 .1377195 0.57 0.570 -.191697 .3481537
married | -2.156323 1.395477 -1.55 0.122 -4.891408 .5787615
_cons | -3.535827 1.661701 -2.13 0.033 -6.792701 -.278953
-------------+----------------------------------------------------------------
married |
sexratio | -.6389774 .1329911 -4.80 0.000 -.8996353 -.3783196
_cons | 1.465433 .1669089 8.78 0.000 1.138298 1.792569
-------------+----------------------------------------------------------------
hazard |
lambda | .6836395 .8193207 0.83 0.404 -.9221996 2.289479
-------------+----------------------------------------------------------------
rho | 0.29763
sigma | 2.2969336
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