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Re: st: mlogit, bootstrap, mfx: "no observations"

From   "Guido Heineck" <[email protected]>
To   [email protected]
Subject   Re: st: mlogit, bootstrap, mfx: "no observations"
Date   Tue, 05 Sep 2006 09:32:43 +0200


sorry for my late reply, I was off for a week. Anyway, just a quick thank you for your additional valuable input and the references you mention. Thanks to Jean and you, I will save a substantial amount of time. 


> On 8/25/06, Guido Heineck <[email protected]> wrote:
> > > IMHO you don't need to bootstrap the multinomial logit either. I
> > > cannot really see the use of it.
> > I am just anticipating the referees' comments?! :-D
> I'd support Jeane. The similarity here is quite superficial: you don't
> really have the first step regression, so you don't have much to
> correct for. Alternatively, if you had access to the original data
> used in NBER, you could run a calibration regression on a bootstrap
> subsample of that data, and use the results on an independent
> bootstrap subsample of your own data; that would make it a valid
> procedure. Otherwise, the bootstrap is no better asymptotically than
> the MLE standard errors. (Feel free to use this piece in an answer to
> the editor as to why you did not do the bootstrap.)
> Other approaches for incorporating the first stage estimates would be:
> (1) Murphy and Topel (1985), doi:10.2307/1391724. They derive an
> estimator which, as far as I can recall, is a sum of two sandwich
> estimators for two stages. If the full covariance matrix is reported
> in the NBER paper you mentioned (I adore it personally when economists
> come to social sciences and teach people what to do and what not to
> do...), then you can use it to correct the standard errors. You can
> even try to feed them back to Stata with -ereturn post- commands,
> which you would have come across if you've written your own
> estimators.
> (2) Bayesian inference, where the reported results can be used as a
> prior on the measurement equation. That would be a pretty complicated
> model, but you can push it through with some help of Bayesian-inclined
> econometricians/statisticians. Basically, this is a rigorous way to
> follow up on Jeane's second suggestion.
> (3) Structural equation models: you have BMI (or whatever) measured
> with error, but you know the properties of the measurement error
> process (SEM folks would say that you know the reliability of your
> measure). See if you can find some sociologists or psychologists to
> talk about your model, as they are more familiar with the types of
> models I am mentioning than ecoomists are (at least in the US, the
> disciplines are somewhat more interpenetrating in Europe).
> Other models for estimation with the measurement error may also be
> appropriate; see a recent book by Carroll, Stefanski, Ruppert and
> Cianicianu -- I think those guys are known in econometric world, too.
> - --
> Stas Kolenikov
Guido Heineck
University of Erlangen-Nuremberg
Department of Economics
Lange Gasse 20
90403 Nuremberg
fon: +49-(0)911-5302-260
fax: +49-(0)911-5302-178
ENER - European Network
on the Economics of Religion

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