----- Original Message -----
From: <[email protected]>
To: <[email protected]>
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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