st: ereturn and sfcross

[Paulo Regis <pauloregis.ar@googlemail.com>]

Dear all,

I am using the new command -sfcross and I would like to know if
someone with some experience with this command could tell me how to
obtain some of the estimated parameters for further manipulation since
it seems they are not available.

Below, you can see an example. I am interested in sigma_u,sigma_v and
lambda. If you check "ereturn list" after the command, you can see
their standard errors are not available. How can I obtain them? For
example, I would like to have them available t  be used with -outreg
or outreg2.

Kind Regards

Paulo

Example:

. webuse greene9

. sfcross  lnv lnk lnl


initial:       Log likelihood = -37.029968
Iteration 0:   Log likelihood = -37.029968  (not concave)
Iteration 1:   Log likelihood = -18.020203  (not concave)
Iteration 2:   Log likelihood = -7.6054699
Iteration 3:   Log likelihood =  1.9182958
Iteration 4:   Log likelihood =  2.8027655
Iteration 5:   Log likelihood =  2.8604121
Iteration 6:   Log likelihood =  2.8604897
Iteration 7:   Log likelihood =  2.8604897

Stoc. frontier normal/exponential model              Number of obs =        25

                       Wald chi2(2)  =    845.68
                                                     Prob > chi2   =    0.0000

Log likelihood =     2.8605
------------------------------------------------------------------------------
         lnv |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
Frontier     |
         lnk |   .2624859   .0919988     2.85   0.004     .0821717    .4428002
         lnl |   .7703795   .1109569     6.94   0.000     .5529079    .9878511
       _cons |   2.069242   .2356159     8.78   0.000     1.607444    2.531041
-------------+----------------------------------------------------------------
Usigma       |
       _cons |  -4.002457   .9274575    -4.32   0.000    -5.820241   -2.184674
-------------+----------------------------------------------------------------
Vsigma       |
       _cons |  -3.527598   .4486176    -7.86   0.000    -4.406873   -2.648324
-------------+----------------------------------------------------------------
     sigma_u |   .1351691   .0626818     2.16   0.031     .0544692    .3354317
     sigma_v |   .1713925   .0384448     4.46   0.000     .1104231    .2660258
      lambda |   .7886525    .087684     8.99   0.000      .616795    .9605101
------------------------------------------------------------------------------

. ereturn list

scalars:
               e(rank) =  5
                  e(N) =  25
                 e(ic) =  7
                  e(k) =  5
               e(k_eq) =  3
               e(k_dv) =  1
          e(converged) =  1
                 e(rc) =  0
          e(k_autoCns) =  0
                 e(ll) =  2.860489723076469
         e(iterations) =  8
               e(chi2) =  845.6810351779072
                  e(p) =  2.3051356429e-184
               e(df_m) =  2
                  e(z) =  -.634477395928109
                e(p_z) =  .2628846569875348
            e(sigma_u) =  .1351691134122321
            e(sigma_v) =  .1713924767932067
             e(lambda) =  .7886525473070807

macros:
            e(cmdline) : "sfcross lnv lnk lnl"
         e(covariates) : "lnk lnl _cons"
            e(cilevel) : "95"
          e(marginsok) : "default xb"
              e(title) : "Stoc. frontier normal/exponential model"
           e(crittype) : "Log likelihood"
               e(dist) : "exponential"
           e(function) : "production"
             e(depvar) : "lnv"
                e(cmd) : "sfcross"
            e(predict) : "sfcross_p"
                e(opt) : "moptimize"
               e(user) : "_cross_exp()"
          e(ml_method) : "lf2"
    e(singularHmethod) : "m-marquardt"
          e(technique) : "nr"
              e(which) : "max"
         e(properties) : "b V"

matrices:
                  e(b) :  1 x 5
                  e(V) :  5 x 5
               e(ilog) :  1 x 20
           e(gradient) :  1 x 5

functions:
             e(sample)
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