RE: st: Re: st: Re: st: RE: Truncated sample or Heckman selection‏

[Ebru Ozturk <ebru_0512@hotmail.com>]

But, what about if I restrict the sample to innovating firms only. Because the research question is related to it. Can't I use tobit regression? There is a mass at zero as many firms do not produce innovation (radical or incremental innovation). By restricting the sample I can also observe Xs.

Ebru

----------------------------------------
> From: millimet@mail.smu.edu
> To: statalist@hsphsun2.harvard.edu
> Subject: RE: st: Re: st: Re: st: RE: Truncated sample or Heckman selection‏
> Date: Fri, 5 Oct 2012 01:08:53 +0000
>
> Yes, in my opinion, if you include the zeros, a fractional logit or tobit or censored LAD is appropriate (given the other assumptions implicit in these models). The only issue is whether some Xs are missing for the zeros. That you will have to confront yourself if you have Xs you want to include that are missing from some obs.
>
> ****************************************************
> Daniel L. Millimet, Professor
> Department of Economics
> Box 0496
> SMU
> Dallas, TX 75275-0496
> phone: 214.768.3269
> fax: 214.768.1821
> web: http://faculty.smu.edu/millimet
> ****************************************************
>
>
> -----Original Message-----
> From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Ebru Ozturk
> Sent: Thursday, October 04, 2012 5:32 PM
> To: statalist@hsphsun2.harvard.edu
> Subject: RE: st: Re: st: Re: st: RE: Truncated sample or Heckman selection‏
>
> Thank you. It will be quite complicated for me to understand this e-mail.
>
> Yes, in my data there is a mass at zero and I include all of them. So you are saying that it is a censoring problem and tobit regression is applicable or a fractional logit model?
>
> The other issue about Xs. The Xs that I am interested in have not been observed for non-innovator firms but there are other Xs that I use them as control variable have been observed for all firms in the sample.
>
> Ebru
>
> ----------------------------------------
> > From: millimet@mail.smu.edu
> > To: statalist@hsphsun2.harvard.edu
> > Subject: RE: st: Re: st: Re: st: RE: Truncated sample or Heckman selection‏
> > Date: Thu, 4 Oct 2012 22:16:57 +0000
> >
> > If you include all firms in a model, with a mass at zero, then is the standard censoring problem. Labor supply models are classic model. Labor supply has a "natural" lower bound at zero, but one does not use OLS. Typically, tobit models are used or semiparametric alternatives like censored LAD or symmetric trimmed least squares. See, for example, Wilhelm (OBES, 2008, "Practical Considerations for Choosing Between Tobit and SCLS or CLAD Estimators for Censored Regression Models with an Application to Charitable Giving"). For percentages, even though these variables are by definition between 0 and 1 (or 100), a fractional logit is the most common model, I believe, if there is a mass at either boundary point.
> >
> > So, in your case, if you include the zeros, yes it is a censoring problem.
> >
> > Th next issue is what Xs you observe for different observations. If all Xs were observed for all obs (0 and positive values), then a fractional logit is the answer (or a tobit or one of the above alternatives). If SOME of the Xs are missing for the obs at zero, then you can (i) drop the zeros and estimate a selection-corrected OLS model - if you ignore the upper limit of 100 - or you can combine the selection correction with a fractional logit/probit model, as long as you are sure the control function term for the correction is correct (this is what some empirical trade papers do when they drop country pairs with zero trade; although it is not recommended), or (ii) include the zeros, but you need two different equations for the zeros and the non-zeros since it sounded like not all Xs are available for the obs at zero. So, something like a hurdle (zero-inflated) model tailored to your example.
> >
> > **********************************************
> > Daniel L. Millimet, Professor
> > Department of Economics
> > Box 0496
> > SMU
> > Dallas, TX 75275-0496
> > phone: 214.768.3269
> > fax: 214.768.1821
> > web: http://faculty.smu.edu/millimet
> > **********************************************
> >
> > ________________________________________
> > From: owner-statalist@hsphsun2.harvard.edu [owner-statalist@hsphsun2.harvard.edu] on behalf of Ebru Ozturk [ebru_0512@hotmail.com]
> > Sent: Thursday, October 04, 2012 4:53 PM
> > To: statalist@hsphsun2.harvard.edu
> > Subject: RE: st: Re: st: Re: st: RE: Truncated sample or Heckman selection‏
> >
> > Innovation success is heavily left-censored - many firms do not have any market novelties and thus no sales from this type of innovation (Grimpe & Kaiser, 2010).
> >
> > Is that wrong then?
> >
> > I'm really confused now.
> >
> > Ebru
> >
> > ----------------------------------------
> > > Date: Thu, 4 Oct 2012 16:45:59 -0500
> > > Subject: st: Re: st: Re: st: RE: Truncated sample or Heckman selection‏
> > > From: joerg.luedicke@gmail.com
> > > To: statalist@hsphsun2.harvard.edu
> > >
> > > On Thu, Oct 4, 2012 at 4:34 PM, Ebru Ozturk <ebru_0512@hotmail.com> wrote:
> > > > For Tobit regression, the dependent variable is the percent of total firm sales revenues that derived from the sales of new products. Therefore, it is censored as sales of new products can only be zero or positive.
> > > >
> > > This just isn't a censoring problem. Consider having a look at:
> > >
> > > http://en.wikipedia.org/wiki/Censoring_%28statistics%29
> > >
> > > Joerg
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