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Re: st: Re: Interpretation of OLS coeff after Heckman selection


From   "Scott Merryman" <smerryman@kc.rr.com>
To   <statalist@hsphsun2.harvard.edu>
Subject   Re: st: Re: Interpretation of OLS coeff after Heckman selection
Date   Fri, 29 Aug 2003 10:54:50 -0500

----- Original Message ----- 
From: <Christer.Thrane@hil.no>
To: <statalist@hsphsun2.harvard.edu>
Sent: Friday, August 29, 2003 10:03 AM
Subject: Re: st: Re: Interpretation of OLS coeff after Heckman selection


> Thanks a lot, Scott!
>
> Christer
>
>

Leave it me to make a solution harder than necessary.

Taking a look at the predict options for heckman, it has an option for E(y | y
observed).

An better way would be to use -mfx compute, pred(ycond) after heckman.

Example


. use http://www.stata-press.com/data/r8/womenwk.dta

. heckman wage educ age, select(married children educ age)

Iteration 0:   log likelihood = -5178.7009
Iteration 1:   log likelihood = -5178.3049
Iteration 2:   log likelihood = -5178.3045

Heckman selection model                         Number of obs      =      2000
(regression model with sample selection)        Censored obs       =       657
                                                Uncensored obs     =      1343

                                                Wald chi2(2)       =    508.44
Log likelihood = -5178.304                      Prob > chi2        =    0.0000

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
wage         |
   education |   .9899537   .0532565    18.59   0.000     .8855729    1.094334
         age |   .2131294   .0206031    10.34   0.000     .1727481    .2535108
       _cons |   .4857752   1.077037     0.45   0.652    -1.625179     2.59673
-------------+----------------------------------------------------------------
select       |
     married |   .4451721   .0673954     6.61   0.000     .3130794    .5772647
    children |   .4387068   .0277828    15.79   0.000     .3842534    .4931601
   education |   .0557318   .0107349     5.19   0.000     .0346917    .0767718
         age |   .0365098   .0041533     8.79   0.000     .0283694    .0446502
       _cons |  -2.491015   .1893402   -13.16   0.000    -2.862115   -2.119915
-------------+----------------------------------------------------------------
     /athrho |   .8742086   .1014225     8.62   0.000     .6754241    1.072993
    /lnsigma |   1.792559    .027598    64.95   0.000     1.738468     1.84665
-------------+----------------------------------------------------------------
         rho |   .7035061   .0512264                      .5885365    .7905862
       sigma |   6.004797   .1657202                       5.68862    6.338548
      lambda |   4.224412   .3992265                      3.441942    5.006881
------------------------------------------------------------------------------
LR test of indep. eqns. (rho = 0):   chi2(1) =    61.20   Prob > chi2 = 0.0000
------------------------------------------------------------------------------

. mfx compute, pred(yc)

Marginal effects after heckman
      y  = E(wage|Zg>0) (predict, yc)
         =  23.136129
------------------------------------------------------------------------------
variable |      dy/dx    Std. Err.     z    P>|z|  [    95% C.I.   ]      X
---------+--------------------------------------------------------------------
educat~n |   .8741544      .04844   18.05   0.000   .779214  .969095    13.084
     age |   .1372695      .01836    7.48   0.000   .101285  .173254    36.208
 married*|   -.962817      .17119   -5.62   0.000  -1.29834 -.627297     .6705
children |  -.9115428      .08565  -10.64   0.000  -1.07942 -.743664    1.6445
------------------------------------------------------------------------------
(*) dy/dx is for discrete change of dummy variable from 0 to 1


I hope this helps even more,
Scott


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