请问进行biprobit回归以后怎样求所有变量的边际效应？

fuzaode

9795

收藏 2013-01-24

请问进行biprobit回归以后怎样求所有变量的边际效应？我试了mfx\margins,不知道如何设定，总是不能得到正确的结果

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蓝色

2013-1-25 07:56:01

webuse school,clear

*Bivariate probit regression
biprobit private vote logptax loginc years

margins,predict(p00) dydx(*)
margins,predict(p01) dydx(*)
margins,predict(p10) dydx(*)
margins,predict(p11) dydx(*)

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fuzaode

2013-1-25 13:45:22

蓝色发表于 2013-1-25 07:56
webuse school,clear

*Bivariate probit regression

试了多次，biprobit回归以后，用margins 总是出现一行提示：prediction is a function of possibly stochastic quantities other than e(b)，这是什么情况呢？

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蓝色

2013-1-25 13:56:59

. webuse school,clear

.
. *Bivariate probit regression
. biprobit private vote logptax loginc years

Fitting comparison equation 1:

Iteration 0: log likelihood = -31.967097
Iteration 1: log likelihood = -31.452424
Iteration 2: log likelihood = -31.448958
Iteration 3: log likelihood = -31.448958

Fitting comparison equation 2:

Iteration 0: log likelihood = -63.036914
Iteration 1: log likelihood = -58.534843
Iteration 2: log likelihood = -58.497292
Iteration 3: log likelihood = -58.497288

Comparison: log likelihood = -89.946246

Fitting full model:

Iteration 0: log likelihood = -89.946246
Iteration 1: log likelihood = -89.258897
Iteration 2: log likelihood = -89.254028
Iteration 3: log likelihood = -89.254028

Bivariate probit regression                      Number of obs =       95
                                                Wald chi2(6) =    9.59
Log likelihood = -89.254028                      Prob > chi2    =    0.1431

------------------------------------------------------------------------------
         |    Coef. Std. Err.    z P>|z|    [95% Conf. Interval]
-------------+----------------------------------------------------------------
private    |
   logptax |  -.1066962 .6669782 -0.16 0.873 -1.413949 1.200557
   loginc | .3762037 .5306484    0.71 0.478    -.663848 1.416255
   years |  -.0118884 .0256778 -0.46 0.643 -.0622159 .0384391
   _cons |  -4.184694 4.837817 -0.86 0.387 -13.66664 5.297253
-------------+----------------------------------------------------------------
vote       |
   logptax |  -1.288707 .5752266 -2.24 0.025 -2.416131 -.1612839
   loginc | .998286 .4403565    2.27 0.023    .1352031 1.861369
   years |  -.0168561 .0147834 -1.14 0.254 -.0458309 .0121188
   _cons |  -.5360573 4.068509 -0.13 0.895 -8.510188 7.438073
-------------+----------------------------------------------------------------
   /athrho |  -.2764525 .2412099 -1.15 0.252 -.7492153 .1963102
-------------+----------------------------------------------------------------
      rho |  -.2696186 .2236753                   -.6346806 .1938267
------------------------------------------------------------------------------
Likelihood-ratio test of rho=0:    chi2(1) =  1.38444 Prob > chi2 = 0.2393

.
. margins,predict(p00) dydx(*)

Average marginal effects                         Number of obs =       95
Model VCE : OIM

Expression : Pr(private=0,vote=0), predict(p00)
dy/dx w.r.t. : logptax loginc years

------------------------------------------------------------------------------
         |          Delta-method
         |    dy/dx Std. Err.    z P>|z|    [95% Conf. Interval]
-------------+----------------------------------------------------------------
   logptax | .408256 .1671983    2.44 0.015    .0805534 .7359587
   loginc |  -.3410811 .1279006 -2.67 0.008 -.5917616 -.0904006
   years | .0062274    .00479    1.30 0.194 -.0031609 .0156157
------------------------------------------------------------------------------

. margins,predict(p01) dydx(*)

Average marginal effects                         Number of obs =       95
Model VCE : OIM

Expression : Pr(private=0,vote=1), predict(p01)
dy/dx w.r.t. : logptax loginc years

------------------------------------------------------------------------------
         |          Delta-method
         |    dy/dx Std. Err.    z P>|z|    [95% Conf. Interval]
-------------+----------------------------------------------------------------
   logptax |  -.3891093 .1823773 -2.13 0.033 -.7465622 -.0316564
   loginc | .2735708 .1424442    1.92 0.055 -.0056147 .5527563
   years | -.004094 .0053094 -0.77 0.441 -.0145002 .0063122
------------------------------------------------------------------------------

. margins,predict(p10) dydx(*)

Average marginal effects                         Number of obs =       95
Model VCE : OIM

Expression : Pr(private=1,vote=0), predict(p10)
dy/dx w.r.t. : logptax loginc years

------------------------------------------------------------------------------
         |          Delta-method
         |    dy/dx Std. Err.    z P>|z|    [95% Conf. Interval]
-------------+----------------------------------------------------------------
   logptax | .0426078 .0636357    0.67 0.503 -.0821158 .1673314
   loginc |  -.0081767 .0477444 -0.17 0.864 -.1017541 .0854006
   years |  -.0003302 .002303 -0.14 0.886    -.004844 .0041836
------------------------------------------------------------------------------

. margins,predict(p11) dydx(*)

Average marginal effects                         Number of obs =       95
Model VCE : OIM

Expression : Pr(private=1,vote=1), predict(p11)
dy/dx w.r.t. : logptax loginc years

------------------------------------------------------------------------------
         |          Delta-method
         |    dy/dx Std. Err.    z P>|z|    [95% Conf. Interval]
-------------+----------------------------------------------------------------
   logptax |  -.0617546 .0669554 -0.92 0.356 -.1929848 .0694757
   loginc | .075687 .0570423    1.33 0.185 -.0361138 .1874878
   years |  -.0018032 .0025059 -0.72 0.472 -.0067147 .0031083
------------------------------------------------------------------------------

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蓝色

2013-1-25 13:57:36

没有问题啊

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fuzaode

2013-1-25 14:47:15

蓝色发表于 2013-1-25 13:57
没有问题啊

感谢版主，我终于发现我的问题了，我的两个方程biprobit (y1=x1+x2+x3+y2) (y2=z1+z2+x1+x2+x3)形式和版主的例子不一样，后面加了force，勉强能出结果了，但是还有个问题不太明白，我要列表报告方程1的边际效应，是用predict（11）的结果还是predict（10）？同理，报告方程2，是用predict（01）还是（00）还是啥东西呢？没有搞明白，还请版主大人指点迷津…