Bioprobit - Beta的协方差矩阵

Question

我在比较穆迪和S & P的信用评级决定因素的权重。 然后做一个测试，测试系数是否与Moody's和S相同P. 我想基于Wald测试来做这件事，但我需要Beta的协方差矩阵。你能帮我用Stata的代码如何获得协方差矩阵吗？

进入模型

变量为S &普拉特MRAT GDP吹胀比等

预先感谢

Answer 1

基于来自Stata data@Nick Cox:

实施例（需要安装bioprobit，这是用户写入命令）

sysuse auto 
bioprobit headroom foreign price length mpg turn 


. bioprobit headroom foreign price length mpg turn 

group(forei | 
     gn) |  Freq.  Percent  Cum. 
------------+----------------------------------- 
      1 |   52  70.27  70.27 
      2 |   22  29.73  100.00 
------------+----------------------------------- 
     Total |   74  100.00 

initial:  log likelihood = -148.5818 
rescale:  log likelihood = -148.5818 
rescale eq: log likelihood = -147.44136 
Iteration 0: log likelihood = -147.44136 
Iteration 1: log likelihood = -147.43958 
Iteration 2: log likelihood = -147.43958 

Bivariate ordered probit regression    Number of obs =   74 
                Wald chi2(4) =  22.61 
Log likelihood = -147.43958      Prob > chi2  =  0.0002 

------------------------------------------------------------------------------ 
      |  Coef. Std. Err.  z P>|z|  [95% Conf. Interval] 
-------------+---------------------------------------------------------------- 
headroom  | 
     price | -.0000664 .0000478 -1.39 0.164  -.00016 .0000272 
     length | .0347597 .013096  2.65 0.008  .009092 .0604274 
     mpg | -.0118916 .0354387 -0.34 0.737 -.0813502 .0575669 
     turn | -.0333833 .0554614 -0.60 0.547 -.1420857 .0753191 
-------------+---------------------------------------------------------------- 
foreign  | 
     price | .0003981 .0001485  2.68 0.007  .0001071 .0006892 
     length | -.0585548 .0284639 -2.06 0.040  -.114343 -.0027666 
     mpg | -.0306867 .0543826 -0.56 0.573 -.1372745 .0759012 
     turn | -.3471526 .1321667 -2.63 0.009 -.6061946 -.0881106 
-------------+---------------------------------------------------------------- 
athrho  | 
     _cons | .053797 .3131717  0.17 0.864 -.5600082 .6676022 
-------------+---------------------------------------------------------------- 
     /cut11 | 2.72507 2.451108      -2.079014 7.529154 
     /cut12 | 3.640296 2.445186      -1.152181 8.432772 
     /cut13 | 4.227321 2.443236      -.561334 9.015975 
     /cut14 | 4.792874 2.452694      -.0143182 9.600067 
     /cut15 | 5.586825 2.480339      .7254488  10.4482 
     /cut16 | 6.381491 2.505192      1.471404 11.29158 
     /cut17 | 7.145783 2.529663      2.187735 12.10383 
     /cut21 | -21.05768 6.50279      -33.80292 -8.312449 
-------------+---------------------------------------------------------------- 
     rho | .0537452 .3122671      -.5079835 .5834004 
------------------------------------------------------------------------------ 
LR test of indep. eqns. :   chi2(1) =  0.03 Prob > chi2 = 0.8636 


# results that are in `Stata's memory` 




ereturn list 

scalars: 
       e(rc) = 0 
       e(ll) = -147.4395814769408 
      e(converged) = 1 
       e(rank) = 17 
        e(k) = 17 
       e(k_eq) = 11 
       e(k_dv) = 2 
       e(ic) = 2 
        e(N) = 74 
     e(k_eq_model) = 1 
       e(df_m) = 4 
       e(chi2) = 22.60944901065799 
        e(p) = .0001515278365065 
       e(ll_0) = -147.4543291018424 
       e(k_aux) = 8 
      e(chi2_c) = .0294952498030625 
       e(p_c) = .8636405133599019 

macros: 
      e(chi2_ct) : "LR" 
      e(depvar) : "headroom foreign" 
      e(predict) : "bioprobit_p" 
       e(cmd) : "bioprobit" 
      e(chi2type) : "Wald" 
       e(vce) : "oim" 
       e(opt) : "ml" 
       e(title) : "Bivariate ordered probit regression" 
      e(ml_method) : "d2" 
       e(user) : "bioprobit_d2" 
      e(crittype) : "log likelihood" 
      e(technique) : "nr" 
     e(properties) : "b V" 

matrices: 
        e(b) : 1 x 17 
        e(V) : 17 x 17 
      e(gradient) : 1 x 17 
       e(ilog) : 1 x 20 

functions: 
      e(sample) 

#You need to use mat list e(V) to display the variance covariance matrix 

mat list e(V) 

symmetric e(V)[17,17] 
        headroom: headroom: headroom: headroom: foreign: foreign: foreign: foreign: 
         price  length   mpg  turn  price  length   mpg  turn 
headroom:price 2.280e-09 
headroom:length -1.431e-07 .00017151 
    headroom:mpg 3.991e-07 .00018914 .0012559 
    headroom:turn 4.426e-07 -.00050302 .00027186 .00307597 
    foreign:price 1.124e-10 -4.999e-09 2.093e-08 2.079e-08 2.205e-08 
foreign:length -5.846e-09 8.021e-06 9.950e-06 -.0000249 -2.087e-06 .00081019 
    foreign:mpg 1.712e-08 .00001035 .00006387 .00001352 1.254e-06 .0006546 .00295746 
    foreign:turn 1.145e-08 -.00002418 .00001022 .00015562 -.00001083 -.00028103 -.0001411 .01746805 
    athrho:_cons 2.360e-07 -.00004531 .0000684 .00005575 -2.010e-06 .00043717 -.00147713 -.00449239 
    cut11:_cons .0000134 .01507955 .07578798 .03653671 1.039e-06 .00068972 .00401168 .00211706 
    cut12:_cons .00001374 .01514192 .07570527 .03630636 9.488e-07 .0007133 .00386727 .00165474 
    cut13:_cons .00001393 .01520261 .07550433 .03603257 9.668e-07 .0007088 .00386171 .00165557 
    cut14:_cons .00001363 .01539981 .07532214 .03582323 1.042e-06 .00068687 .00392914 .00189195 
    cut15:_cons .00001264 .01584186 .07541396 .03541453 1.101e-06 .00068091 .0040106 .00209853 
    cut16:_cons .00001148 .01611862 .07562328 .03535426 1.052e-06 .00069849 .00401805 .00206701 
    cut17:_cons .00001055 .01602514 .07547739 .03620485 9.866e-07 .00069868 .00399718 .00207143 
    cut21:_cons 4.412e-07 .00073781 .00377201 .00190456 -.00058242 .13231539 .18778679 .51179829 

        athrho:  cut11:  cut12:  cut13:  cut14:  cut15:  cut16:  cut17: 
         _cons  _cons  _cons  _cons  _cons  _cons  _cons  _cons 
    athrho:_cons .09807649 
    cut11:_cons -.0064343 6.0079319 
    cut12:_cons .00229188 5.9652808 5.9789347 
    cut13:_cons .00187855 5.9546524 5.9639617 5.9694026 
    cut14:_cons -.00310632 5.9724552 5.9793328 5.9820512 6.0157096 
    cut15:_cons -.00783593 6.0300908  6.03522 6.0360956 6.0667389 6.1520838 
    cut16:_cons -.00756313 6.0745198 6.0789515 6.0788816 6.1081885 6.1880183 6.275988 
    cut17:_cons -.00673882 6.0811477 6.0851101 6.0844209 6.1128719 6.1897756 6.2679698 6.3991936 
    cut21:_cons -.13478036 .30582954 .28918756 .28844026 .29527602 .30401845 .30575462 .30503648 

         cut21: 
         _cons 
    cut21:_cons 42.286275 

# If you want to use variance covariance matrix of first four variables 

mat kk=e(V) 
mat kkk=kk[1..4,1..4] 
mat list kkk 

symmetric kkk[4,4] 
        headroom: headroom: headroom: headroom: 
         price  length   mpg  turn 
headroom:price 2.280e-09 
headroom:length -1.431e-07 .00017151 
    headroom:mpg 3.991e-07 .00018914 .0012559 
    headroom:turn 4.426e-07 -.00050302 .00027186 .00307597