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Re: st: different standard errors with gllamm vs. xtmelogit


From   Stas Kolenikov <skolenik@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: different standard errors with gllamm vs. xtmelogit
Date   Wed, 27 Oct 2010 11:58:25 -0400

The jump of 1 in the likelihood is not nice (on part of -xtmelogit-),
but the jump of 3 for -gllamm- is certainly indicative of lack of
numeric accuracy. I would continue increasing the number of points
(and invoking -adapt- option, per Bobby G's suggestion, is almost
always a good idea).

Are z-statistics for the variables of interest (about) the same?
Sometimes, there are weird scaling issues in multilevel models with
limited dependent variables (see
http://www.citeulike.org/user/ctacmo/article/3057661).

On Wed, Oct 27, 2010 at 6:24 AM, de Vries, Robert
<r.de-vries08@imperial.ac.uk> wrote:
> The log likelihood's with 7 integration points are:
>
> Xtmelogit: -15047.008
> Gllamm:  -15056.655
>
> I tried increasing the number of integration points to 15 as you suggested. This yielded yet more strange results.
>
> The gllamm and xtmelogit likelihood's are more similar for these models, but with gllamm's still being slightly higher (15047.916 vs. 15059.682)
>
> The standard errors of the coefficient I'm interested in are still wildly different between gllamm and xtmelogit, but now so is the coefficient itself:
>
> Xtmelogit: -.034(.039663)
> Gllamm: -.0045(.004751)
>
> Most of the other coefficients and standard errors are almost identical between the two models so initially I thought it might just be a problem with the level 2 variable I've been discussing above. However the coefficients and SE's of the individual level binary gender variable are also very different between gllamm and xtmelogit.
>
> I'm stumped...

-- 
Stas Kolenikov, also found at http://stas.kolenikov.name
Small print: I use this email account for mailing lists only.

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