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Re: R: st: intreg with control for sample selection and endogeneity bias


From   "Justina Fischer" <JAVFischer@gmx.de>
To   statalist@hsphsun2.harvard.edu
Subject   Re: R: st: intreg with control for sample selection and endogeneity bias
Date   Tue, 30 Oct 2012 00:41:45 +0100

Hi Francesco

obtaining bootstrapped s.e. should be an option of -intreg-.


For the first stage: Practically a good model specification strategy (instrument, exclusion restriction!) with supportive statistical tests should do at least for the first stage.

For the second stage you need to consult an intreg-specialist which I am not....
E.g. I do not know whether the assummption of the joint normality of the error terms still prevails.

HTH

Justina
-------- Original-Nachricht --------
> Datum: Tue, 30 Oct 2012 00:14:08 +0100
> Von: "Francesco Pastore" <francesco.pastore@unina2.it>
> An: statalist@hsphsun2.harvard.edu
> Betreff: R: st: intreg with control for sample selection and endogeneity bias

> Hi, Justina,
> any more details about bootstrapping and the rest? How would you suggest
> doing it practically.
> Francesco
> 
> -----Messaggio originale-----
> Da: owner-statalist@hsphsun2.harvard.edu
> [mailto:owner-statalist@hsphsun2.harvard.edu] Per conto di Justina Fischer
> Inviato: 30 October 2012 00:09
> A: statalist@hsphsun2.harvard.edu
> Oggetto: Re: st: intreg with control for sample selection and endogeneity
> bias
> 
> Hi Francesco
> 
> 
> when pursuing Thierry's suggesion do not forget to get the standard errors
> of the intreg regression right; I think bootstrapping is the easiest.
> 
> In general, make sure that the statistical assumptions of both stages are
> met since they are important for identification and consistency.
> 
> Justina
> 
> -------- Original-Nachricht --------
> > Datum: Mon, 29 Oct 2012 23:12:45 +0100
> > Von: urbain thierry YOGO <yogout@gmail.com>
> > An: statalist@hsphsun2.harvard.edu
> > Betreff: Re: st: intreg with control for sample selection and 
> > endogeneity bias
> 
> > hi
> > i think you can deal with the sample selection as usual : you estimate 
> > an equation of labor market participation, you recover the predicted 
> > value p and you  compute the inverse Mills ratio as follow:
> > gen pdfp1=normalden(p1)
> > gen cdfp1=normprob(p1)
> > gen IMR=pdfp1/cdfp1
> > finally, you insert IMR in the wage equation.
> > 
> > 2012/10/29, Francesco Pastore <francesco.pastore@unina2.it>:
> > > Hi, All,
> > > I would like to ask to the statalist the following question:
> > >
> > > I am estimating a wage equation to see the impact of overeducation 
> > > on earnings of a sample of graduates observed 5 years after getting 
> > > their degree. The dependent variable is defined as an interval data: 
> > > people
> > are
> > > asked to say to which class of earnings does their income belong to,
> > rather
> > > than a precise value. In this case, I should use the intreg command. 
> > > One problem is that these types of cross-section estimates are 
> > > plagued by sample selection and endogeneity bias. Does anybody know 
> > > how to deal with it in the contest of interval regressions? Is there 
> > > any routine available in
> > Stata?
> > >
> > > Best regards
> > > Francesco
> > >
> > >
> > >
> > > *
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> > >
> > 
> > 
> > --
> > *Urbain Thierry YOGO
> > Ph.D candidate in Economics*
> > *
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