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RE: st: bootstrap with mixed logit


From   "Marco N. Anterpi" <[email protected]>
To   <[email protected]>
Subject   RE: st: bootstrap with mixed logit
Date   Fri, 27 Jan 2012 18:48:36 +0100

You are right about the standar errors for the different classes, especially with many classes.I try to re-express my problem.
I would like to get summary statistics for the distribution of each coefficient and compute standard errors of these statistics.
For example, I would like the standard errors for the mean of the coefficient of price.
What I do is: 
bootstrap  t=r(mean), rep(500): sum price
However, I think I am making a mistake there.

Thank you

----------------------------------------
> Date: Fri, 27 Jan 2012 12:06:35 -0500
> Subject: Re: st: bootstrap with mixed logit
> From: [email protected]
> To: [email protected]
>
> I would not expect this work, as the class labels will always be
> different in different bootstrap samples: what is class 1 in the
> original sample may be class 3 in the first bootstrap subsample, and
> class 4 in the second one, etc. With a single run, a permutation of
> class labels is irrelevant, but matching the classes over repeated
> runs, as is needed for the bootstrap, is extremely cumbersome.
> Moreover, some smaller classes may disappear if they don't have enough
> observations in a given bootstrap sub-sample. So I don't think the
> bootstrap is a sensible method for this problem.
>
> On Fri, Jan 27, 2012 at 8:42 AM, Marco N. Anterpi <[email protected]> wrote:
> > Hello everyone,
> >
> > I have an unbalanced panel with a binary dependent
> > variable and I estimated a mixed logit model. However, I do not manage
> > to compute the standard errors for the summary statistics with the
> > command bootstrap.
> >
> > More in detail, I estimated a mixed logit model with 5 latent classes via EM algorithm,
> > using the command:
> >  lclogit choice price income gender, group(ind) id(dec) nclasses(5)
> >
> > I can display the list of parameters
> > (matrix B) and the probability of class membership (matrix P) and 5
> > variance-covariance matrices of the estimators ( v1 v2 v3 v4 v5 one for each class).
> >
> > My problem is that I do not manage to produce the standard errors
> > for the summary statistics of the different variables and I am probably
> > making a silly mistake in handling the data.
> >
> > Can anyone tell me the exact steps I should follow?
>
> --
> Stas Kolenikov, also found at http://stas.kolenikov.name
> Small print: I use this email account for mailing lists only.
>
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