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[no subject]
I don't know what this means.
To Jeph, as stated it is indeed a fixed effects model. Stata
automatically removes groups that have only positive or negative
values, e.g.:
note: 5 groups (344 obs) dropped because of all positive or
all negative outcomes.
Could you elaborate on what exactly you mean with panel level
variables, please? I don't want to respond based on somewhat different
interpretations.
To Maarten, thanks for making clear that the problem is not in the
double precision. It looks like xtdata got me on the wrong path.
I have been working on this problem for over a week now, and I hope
that we can get it solved together.
Thanks again. I am looking forward to your insights.
Tom
On Tue, Apr 2, 2013 at 2:45 PM, Klaus Pforr <[email protected]> wrote:
> <>
> Dear Tom,
>
> have you tried to estimate it with clogit? Does the problem also occur
> there? Could give us a little bit more detail about the error message?
>
> best
>
> Klaus
> __________________________________
>
> Dr. Klaus Pforr
> GESIS -- Leibniz Institut für Sozialwissenschaft
> B2,1
> Postfach 122155
> D - 68072 Mannheim
> Tel: +49 621 1246 298
> Fax: +49 621 1246 100
> E-Mail: [email protected]
> __________________________________
>
> -----Ursprüngliche Nachricht-----
> Von: [email protected]
> [mailto:[email protected]] Im Auftrag von JVerkuilen
> (Gmail)
> Gesendet: Dienstag, 2. April 2013 14:29
> An: [email protected]
> Betreff: Re: st: xtlogit: panel data transformation's recast to double makes
> model incomputable
>
> On Tue, Apr 2, 2013 at 8:24 AM, Tom <[email protected]> wrote:
>
>> Insofar noone has explained why the computation is unsuccessful. It'd
>> be especially helpful if someone could explain why less precision
>> would allow the maximizer to do it's job.
>
> Well I don't know but one direction to check is that that is a huge dataset.
> So try randomly subsampling down to, say 10% and see if that works. Maybe
> you're running into some weird memory or matrix size problem. Usually Stata
> would appropriately whine, but who knows.
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