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RE: st: IV with oprobit / mprobit?


From   "Tobias Hofmann" <hofmannt@gmail.com>
To   <statalist@hsphsun2.harvard.edu>
Subject   RE: st: IV with oprobit / mprobit?
Date   Sat, 18 Mar 2006 14:25:44 +0100

Dear Judith,

Well, and? Could you or would you, please, elaborate on this rahter shortish
remark? Thanks!

Have a nice weekend,
Tobias 


-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu On Behalf Of Abrams, Judith
Sent: Friday, March 17, 2006 6:01 PM
To: statalist@hsphsun2.harvard.edu
Subject: RE: st: IV with oprobit / mprobit?

I did.


-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu On Behalf Of Austin Nichols
Sent: Wednesday, March 15, 2006 1:57 PM
To: statalist@hsphsun2.harvard.edu
Subject: Re: st: IV with oprobit / mprobit?

I have also not done the algebra, but I sincerely doubt that the example
Tobias coded, where one excluded instrument is used to identify the effects
of two endogenous dummy variables, can possibly be right.

A model in which the endogenous regressor is an ordered categorical variable
and the dependent variable is continuous can be fitted using -ivreg-, since
its consistency does not depend on the endog var having a particular
distribution, and trading a tiny efficiency gain for a well-understood
estimator, with no known errors in the code, is well worth it, IMHO.

See -help _robust- and [P] _robust for help on robust var and clusters.

On 3/15/06, Brian P. Poi <bpoi@stata.com> wrote:
>   (message trimmed)
In your program, the first stage is fit via -oprobit- and the second stage
via -regress-, which implies to me that you are envisioning a model in which
the endogenous regressor is an ordered categorical variable and the
dependent variable is continuous.

If you are interested in a model like -ivprobit- with an ordered dependent
variable, then the two-step estimator of Rivers and Vuong for probit (1988,
Journal of Econometrics) could probably be extended in a straightforward
way.  Newey's efficient estimator (1987, Journal of
Econometrics) might also be a viable option, though it would a bit more work
to code, since it makes use of a two-step estimator like Rivers and Voung's.
The maximum likelihood estimator as used by -ivprobit- could also be
generalized.  (These ideas should be taken as conjecture -- in principle
they should work, though I haven't done the algebra to guarantee that they
will work or are practical to implement.)

If, on the other hand, you mean a model where the endogenous regressor is an
ordered categorical variable, then I don't have anything to add, other than
a guess that the treatment effects literature may have something to say.


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