Claudio
-mixlogit- will let you run the same model as your -gllamm- model, the
only difference is that -mixlogit- uses simulation rather than
quadrature to approximate the log likelihood function. As pointed out
by Jay quadrature can be very slow when the dimension of the integral
is high (and unfortunately 3 is already 'high'). With your problem
-mixlogit- is likely to be relatively slow too, however, as you've got
a fairly large sample, but should be considerably faster than
-gllamm-.
I have a paper describing the -mixlogit- command which was published
in the Stata Journal last year (7-3). The second example in the paper
is very similar to your model. If you don't have access to the SJ a
pre-publication draft is available here:
<http://www-users.york.ac.uk/~ah522/mixlogit.pdf>
Arne (author of -mixlogit-)
On 29/10/2008, Stas Kolenikov <[email protected]> wrote:
> On 10/29/08, Claudio Cruz Cazares <[email protected]> wrote:
> > When using mprobit with the cluster option if enough for controlling for the unobservable hetereogenity in the panel? Is the unobservable hetereogenity and correlation between observations are controlled in mixlogit since the "group" and "id" options are specified?
>
> No, all the -cluster- options do is they correct the estimating
> equations for possible correlations between individuals. If you want
> to take those into account more accurately you need to come up with
> more complicated models. Think of the difference between linear
> regression and panel data models: you can do -cluster- with regress,
> but that will still give you biased point estimates if you have random
> effects correlated with regressors. Same story here.
>
> --
> Stas Kolenikov, also found at http://stas.kolenikov.name
> Small print: I use this email account for mailing lists only.
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