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st: Re: Lower boundary on proportion of observations uncensored in heckprob, plus how to deal with interactions in selection and/or outcome models


From   "Martin Weiss" <[email protected]>
To   <[email protected]>
Subject   st: Re: Lower boundary on proportion of observations uncensored in heckprob, plus how to deal with interactions in selection and/or outcome models
Date   Sat, 8 Nov 2008 15:11:43 +0100

"If it would help I could reproduce the output here."

It would be better if you could frame your question in terms of the example in -h heckprob-...

HTH
Martin
_______________________
----- Original Message ----- From: "Tyler, Margaret C D" <[email protected]>
To: <[email protected]>
Sent: Friday, November 07, 2008 11:53 PM
Subject: st: Lower boundary on proportion of observations uncensored in heckprob, plus how to deal with interactions in selection and/or outcome models


Hello Statalisters.
I am using probit with selection (heckprob) for the first time. I'm having a lot of fun (as usual when working with Stata!), but I'm concerned that I am
misinterpreting my findings and/or using the technique incorrectly.  So I
have a couple of quick (well, OK maybe not so quick) questions. I would
greatly appreciate any help.

I am working with data from a survey on which we unfortunately were able to
achieve only an 18.2% response rate for the 2 outcomes of interest in our
current analysis. (This was not unexpected for our population--low response
rates have become the norm for this group, although the 18% was
disappointing.) We have a total of 6412 observations, with 5246 censored and
1166 uncensored. The selection model does contain one variable (gender)
which is not included in the outcome model (not significant in outcome
model). Rho is not significant.  Wald chisq(18)=95.05, p =0.0000.

My first question is this: is there a minimum response rate or proportion of uncensored observations under which Heckman or similar types of methods are
not appropriate? And if so, what would that be?

I am quite confused about using and interpreting interaction effects in
heckprob.  There are several significant interactions in my outcome model.
One of these interactions is significant in predicting selection as well. It seems sort of incorrect to have the same interaction term in both equations
although I can't figure out why I think that. If an interaction term is
significant in the selection equation alone and in the outcome equation
alone, is it more appropriate to include it in the selection equation when
running heckprob?

If it would help I could reproduce the output here.
Thank you so much...
Margaret Tyler
University of Iowa









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