Notice: On April 23, 2014, Statalist moved from an email list to a forum, based at statalist.org.

[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

From |
Ebru Ozturk <ebru_0512@hotmail.com> |

To |
<statalist@hsphsun2.harvard.edu> |

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

Date |
Fri, 5 Oct 2012 00:17:42 +0300 |

But, the dependent variable includes many zero values as many firms do not produce innovation and of course no sales from this tyep of innovation. Also, published papers have used Tobit regression and Heckman two step correction with the same data, are they all wrong then? Ebru ---------------------------------------- > Date: Thu, 4 Oct 2012 20:54:49 +0100 > Subject: st: Re: st: RE: Truncated sample or Heckman selection > From: njcoxstata@gmail.com > To: statalist@hsphsun2.harvard.edu > > I agree on #1. > > On #2, how is Ebru going to fit any kind of model with no data on predictors? > > Nick > > On Thu, Oct 4, 2012 at 7:50 PM, Millimet, Daniel <millimet@mail.smu.edu> wrote: > > 1. A fractional logit model is more appropriate when modeling percentages. > > 2. The data set up is in between the usual Heckman vs. truncated model setup. With the typical Heckman approach, X's are observed for all observations and there is no information on the missing outcome. With a truncated setup, we observe no X's, but have information at least on the range of values for the outcome. Here, you do know something about the value of Y for the censored observations as in the truncated setup, but you only observe a subset of the X's. To me, it sounds you could perhaps "invent" a new model that is a zero-inflated fractional logit model, since you have 1 set of regressors that impact perhaps the probability of no innovation, and then a second set of regressors that impacts the amount of innovation conditional on this being positive. > > > > Anyway, perhaps not the best answer. > > > > > > -----Original Message----- > > From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Ebru Ozturk > > > > I have a question that I cannot decide whether I should use truncated regression or Heckman sample selection. > > For instance, in the dataset, firms that produce any type of innovation (process or product) give information about other 'x' variables. In other words, firms that do not produce any innovation do not answer other questions as these questions are directly related to firms' innovation activities. So, the 'x' variables that I am interested in have no values only for those firms that do not produce innovation. But, I know the dependent (y) variable in both case, either firms produce innovation or not produce. > > > > > > I am planning to run tobit regression as the dependent variable is percentage between 0 - 100 and Heckman sample selection model to check selection bias. But, I can not decide whether it is truncated sample or Heckman sample selection. > > > > So, what do you think? > > > > Thank you very much, Ebru. > > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/faqs/resources/statalist-faq/ > * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**st: Re: st: Re: st: RE: Truncated sample or Heckman selection***From:*Joerg Luedicke <joerg.luedicke@gmail.com>

**References**:**st: Truncated sample or Heckman selection***From:*Ebru Ozturk <ebru_0512@hotmail.com>

**st: RE: Truncated sample or Heckman selection***From:*"Millimet, Daniel" <millimet@mail.smu.edu>

**st: Re: st: RE: Truncated sample or Heckman selection***From:*Nick Cox <njcoxstata@gmail.com>

- Prev by Date:
**st: Weighted proportion** - Next by Date:
**st: Problem with setting the right reference category when using mlogit** - Previous by thread:
**st: Re: st: RE: Truncated sample or Heckman selection** - Next by thread:
**st: Re: st: Re: st: RE: Truncated sample or Heckman selection** - Index(es):