Re: st: Selection with an endogenous ordinal variable

[Filipe Silva <filipeourico@googlemail.com>]

Thank you for the suggestion on the -cmp-

I apologise for not being as clear as I should have been

I intend to measure the impact of firms' financial constraints (FC)
upon R&D investment:
y: R&D investent, whith 74% of zeroes,   €{0}U]0, +00[
main x: FC, which is ordinal,  €{0;1;2;3}

Since there are so many zeroes, I hypothesized that there is first the
decision to invest or not (RD) followed by the decision on the amounts
invested if RD==1. Accordingly, if these decisions are assumed to be
independent (conditional on observable vars.),  I'd estimate a 2part
"hurdle" with no need to jointly specify errors.
However, I tested a -heckman- not accounting for endogeneity and it
appears that there are unobservables affecting both errors.
As a result I thought of a selection model, but please correct me if
wrong (I have just recently started to deal with such kind of data).

Additionally, there are reasons to believe that FC is endogenous in
the RD_I decision, which further complicates.

Thank you very much,
Filipe



2011/2/8 Austin Nichols <austinnichols@gmail.com>:
> Filipe Silva <filipeourico@googlemail.com>:
> I don't understand your problem statement--can you clarify what the outcome and
> endogenous ordinal var are, and what form of selection is hypothesized?
> You may also want to look at -cmp- on SSC for MLE options.
>
> On Tue, Feb 8, 2011 at 5:57 AM, Filipe Silva
> <filipeourico@googlemail.com> wrote:
>> Dear all,
>>
>> I am currently trying to estimate a model of selection, where the main
>> explanatory variable in this model is endogenous (and ordinal!). All
>> of them observed.
>> This should be easily done with -heckman-, if not for the endogenous regressor.
>>
>> I wonder if there is any package that can handle (MLE) estimation of such model?
>>
>> Alternatively I have considered a two-step procedure (probit, obtain
>> mills, use in ivreg2 to account for endogeneity- as far as I've
>> understood this is the procedure described in Wooldridge's textbook,
>> 2002 pp. 567-570 as an extension to "heckit", even though I'm not
>> totally sure it applies to an ordinal endogenous var.) but the the
>> Variance correction in the second step is rather hard to compute. I
>> have considered using the bootstrap for such correction.
>>
>> Could anyone please advise me if this reasoning is correct or if there
>> is an alternative way to do it?
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