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st: Using cmp with many models


From   "Tysinger, Bryan" <btysinge@healthpolicy.usc.edu>
To   "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>
Subject   st: Using cmp with many models
Date   Sun, 29 Jul 2012 06:26:14 +0000

Hi there!  My goal is to use cmp to estimate the covariance of the error terms for about 20 models.  I've into some issues.

The first issue I'm dealing with is a variable with two levels of selection.  I have a model for work status, a model for having a pension, and a model for pension wealth.  In my data, pension wealth is only observed if a person has a pension and pension status is only observed if a person is working.  Consequently, I have something like this in mind:

#delimit ;
cmp (pension_level = `vars1') (any_pension = `vars2') (working = `vars3') 
, indicators( any_pension*$cmp_cont working*$cmp_probit $cmp_probit )  
  ghkdraws(10) tech(dfp) ;
#delimit cr

I can't get this to estimate.  I get the following error message:

Fitting constants-only model for LR test of overall model fit.
         dPhi_dpE_dSig():  3200  conformability error [11]
      cmp_lnL_censored():     -  function returned error [80]
               cmp_lnL():     -  function returned error [433]
                 <istmt>:     -  function returned error
Mata run-time error
r(3200);

Is this possible to estimate using cmp?  What should I try to get this to work?

Ultimately, I have about 17 other "seemingly unrelated" models I would like to include in this estimation (health outcomes and other economic outcomes).  I think cmp is the appropriate routine to use, but I haven't had much luck getting more than a half dozen models to estimate simultaneously and converge to a solution, even when I skip the models with selection issues.  Any tips?

Thanks in advance,
Bryan Tysinger


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