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

[Austin Nichols <austinnichols@gmail.com>]

Ebru Ozturk <ebru_0512@hotmail.com>:
Start with "Inference for partial effects in nonlinear panel-data
models using Stata"
by Jeffrey Wooldridge, linked from
http://www.stata.com/meeting/snasug08/abstracts.html

On Fri, Oct 5, 2012 at 12:51 PM, Nick Cox <njcoxstata@gmail.com> wrote:
> Much of the point of this thread is that those answering you
> _disagree_ on certain key things and are at least in part_unclear_ on
> your situation.
>
> I already said "If you are expecting that Statalist can act as a
> collective oracle that can tell you the _correct_ thing to do in your
> project, that is I suggest more than the list can deliver."
>
> So, you still think you can get definitive advice out of us?
>
> 0. What do your advisors/supervisors/colleagues say who know more
> about your dataset and objectives?
>
> 1. I think that's overstating it. It just sounds like a natural model
> but no-one has access to your data or awareness of your full
> objectives to be able to say how well it will work.
>
> 2. That's overstating it too. Whether you _need_ to do Heckman is your
> decision.
>
> 3. I don't know what this means. See #0.
>
> 4. You need to try it. Answering this question would mean going back
> to Stata 10 and looking at the documentation and I can't answer off
> the top of my head. (As you didn't say, contrary to advice, that you
> are using an old version of Stata, no-one will have been thinking
> about that.)
>
> 5. I don't see why not in principle, but you may need to write a
> program in practice. All depends on how the panel aspect is handled.
>
> Nick
>
> On Fri, Oct 5, 2012 at 5:38 PM, Ebru Ozturk <ebru_0512@hotmail.com> wrote:
>>
>> Thank you very much for your help. I just would like to make sure that I understand you correctly. Also I am here to learn so please forgive me for asking many questions.
>>
>> 1. I restrict sample to innovating firms and the dependent variables that I am interested in are percentages therefore, you agree on that I should use fractional logit model.
>>
>> 2. I do not need to do Heckman sample selection even though I restrict my sample. I also want to mention that yes I do not have some Xs but I still do have other Xs for non-innovators which I use them as control variables.
>>
>> 3. Do you think using a different model puts me trouble?
>>
>> 4. I use Stata 10 so can I use fractional logit model?
>>
>> 5. What about if I have a panel data? Is fractional logit model still applicable?
>>
>> Ebru
>>
>> ----------------------------------------
>>> Date: Fri, 5 Oct 2012 16:21:40 +0100
>>> Subject: st: Re: st: Re: st: Re: st: Re: st: Re: st: RE: Truncated sample or Heckman selection‏
>>> From: njcoxstata@gmail.com
>>> To: statalist@hsphsun2.harvard.edu
>>>
>>> Isn't this the same question?
>>>
>>> It seems to me that you have two issues, which are independent of each other.
>>>
>>> 1. Your response is bounded [0,1] or [0,100], dependent on units.
>>>
>>> Joerg, Maarten and I all seem to agree that this is not censoring in
>>> any strong sense, as values outside the bounds are impossible in
>>> principle. Thus we don't think that tobit makes sense here.
>>>
>>> Daniel's argument appears to be that there is a important sense in
>>> which this can be considered as censoring. Thus he seems more open to
>>> the idea that tobit makes sense.
>>>
>>> This is not an election and what is right is not determined by
>>> counting votes. As I don't understand Daniel's argument I certainly
>>> can't refute it.
>>>
>>> Personally I pay little attention to what's popular in the literature
>>> if I think it's wrong. It's good news for me: I have a chance to try
>>> to improve practice.
>>>
>>> 2. There is a question of what determines which firms do and which do
>>> not innovate. This is interesting and important and you should model
>>> it _if you have data to do so_. In your earliest posts in this thread,
>>> you seemed (to me at least) to imply that you have no other data on
>>> non-innovating firms with which to model selection, but you should
>>> know.
>>>
>>> This is just my summary. The others who have contributed to the thread
>>> may want to correct or complement it. Other people of course may have
>>> other views.
>>>
>>> If you are expecting that Statalist can act as a collective oracle
>>> that can tell you the _correct_ thing to do in your project, that is I
>>> suggest more than the list can deliver.
>>>
>>> Nick
>>>
>>> On Fri, Oct 5, 2012 at 3:29 PM, Ebru Ozturk <ebru_0512@hotmail.com> wrote:
>>> > My question was whether I should use Heckman sample selection as I restricted my sample to innovating firms only.
>>> >
>>> > After discussing with you, I also confused as I said before many papers used tobit regression and did Heckman correction and never seen fractional logit model in these papers such as Grimpe & Kaiser, (2010); Cassiman & Veugelers, (2006).
>>> >
>>> > These papers are saying that, the data is left censored as it includes many zeros. Also when I search for fractional logit model it says it is bounded between 0 and 1 but my dependent variable includes for instance, 0, 11, 13, 15, 25 values and so on.
>>> >
>>> > Maybe I am not very good at statistics but when I read papers they use tobit regression. Whey then?
>>> >
>>> > Ebru
>>> >
>>> > ----------------------------------------
>>> >> Date: Fri, 5 Oct 2012 15:02:22 +0200
>>> >> Subject: st: Re: st: Re: st: Re: st: Re: st: RE: Truncated sample or Heckman selection‏
>>> >> From: maartenlbuis@gmail.com
>>> >> To: statalist@hsphsun2.harvard.edu
>>> >>
>>> >> On Fri, Oct 5, 2012 at 1:53 PM, Ebru Ozturk <ebru_0512@hotmail.com> wrote:
>>> >> > I think there is a misunderstanding here. Yes, I have valid and meaningful values on my explanatory variables but when I restrict the sample to innovating firms. By restricting the sample I just exclude the firms that no activity toward innovation at all which I am not interested in them. So, in the restricted sample I can also observe Xs for 0s as my dependent variables are radical and incremental innovation.
>>> >> >
>>> >> > I hope it is clear now.
>>> >>
>>> >> Now I really don't understand your problem: Is it that you have a
>>> >> dependent variable that is a proportion and some of these proportions
>>> >> are 0? Than just use a fractional logit model (just -glm- with the
>>> >> -link(logit)- and the -vce(robust)- options). For such problems
>>> >> neither -tobit- nor -heckman- are appropriate nor is there any
>>> >> truncation going on.

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