用biprobit求联立递归probit方程组可不可以？

3096

收藏 2015-05-13

联立递归probit方程组  Y=a0+a1*Z1+a2*Z2
                                 X=b0+b1*Y+b2*Z1+b3*Z2
y1和x1是两个虚拟变量,Z是影响变量，Z1是虚拟变量，Z2是连续变量，要求这个方程组的参数，我用了biprobit命令，

biprobit (Y= Z1 Z2) (X= Y Z1 Z2), robust

得到结果，不知道用biprobit 进行参数估计对不对
Seemingly unrelated bivariate probit             Number of obs =       24
                                                                  Wald chi2(4) =       .
Log pseudolikelihood = -31.053363                Prob > chi2    =       .

------------------------------------------------------------------------------
         |             Robust
         |    Coef. Std. Err.    z P>|z|    [95% Conf. Interval]
-------------+----------------------------------------------------------------
Y          |
      Z1 | .838012 .8013423    1.05 0.296 -.7325901 2.408614
      Z2 |  -.1849706 .2923726 -0.63 0.527 -.7580105 .3880692
   _cons | .0868316 .900504    0.10 0.923 -1.678124 1.851787
-------------+----------------------------------------------------------------
X          |
         Y | 1.774697 .3756263    4.72 0.000    1.038483 2.510911
      Z1 | -.046001 .655502 -0.07 0.944 -1.330761 1.238759
      Z2 |  -.0195191 .2586773 -0.08 0.940 -.5265173    .487479
   _cons | -.743766 .7754223 -0.96 0.337 -2.263566 .7760337
-------------+----------------------------------------------------------------
   /athrho |  -2.894898 .5587284 -5.18 0.000 -3.989985 -1.79981
-------------+----------------------------------------------------------------
      rho |  -.9939014 .0067941                   -.9993157 -.9467863
------------------------------------------------------------------------------
Wald test of rho=0:                chi2(1) =  26.8451 Prob > chi2 = 0.0000

扫码加我拉你入群

请注明：姓名-公司-职位

以便审核进群资格，未注明则拒绝

全部回复

lxfkxkr

2016-6-22 15:04:42

Recursive Bivariate Probit model
„ Fist equation: Y1 =Y2 X1 X2
„ Second equation: Y2 = X1 X2 X3
.biprobit (Y2 = X1 X2 X3) (Y1 = Y2 X1 X2), robust

Obtain marginal effects
a. Direct effect in the second equation (if X1 is dummy):
mfx compute, at(mean Y2=1, X1=1) predict(pmarg1)
Î To save time, use “nose” command if you don’t need to get S.E.
b. Direct/Indirect effects in the first equation: Since the way of obtaining these effects
by using “mfx” commands is not clear, I recommend to read the Stata manual or
write your own “do-file”. Also refer to Greene’s “Economic Analysis” and his
article, “Gender Economics Courses in Liberal Arts Colleges: Comment”, from his
web site.