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st: different standard errors with gllamm vs. xtmelogit


From   "de Vries, Robert" <r.de-vries08@imperial.ac.uk>
To   "'statalist@hsphsun2.harvard.edu'" <statalist@hsphsun2.harvard.edu>
Subject   st: different standard errors with gllamm vs. xtmelogit
Date   Tue, 26 Oct 2010 14:11:40 +0100

Hello everyone. I'm having a weird problem with gllamm and xtmelogit when r= unning a fairly simple 2-level random intercepts model.

The model is predicting a binary health outcome from 1 level 2 variable (me=
angini) and several level 1 variables. It is a sample if 5,4410 people in 1=
6 countries, with 'country' as the level 2 cluster.

The xtmelogit model looks like this:

xi: xtmelogit poorhealth meangini age47 gndr i.education if poorhealth_samp=
le=3D=3D1 || country1 :

(note that the number of integration points is left at the default of 7)

It converges fine on iteration 3 with a log likelihood if -15046.989. The r= esult I am interested in is for meangini and in this model it is -0.030 (SE=  =3D 0.035)

The gllamm model is identical (as far as I can tell)

xi: gllamm poorhealth meangini age47 gndr i.education if poorhealth_sample= =3D=3D1, i(country1) nip(7) link(logit) f(binomial)

However the coefficient for the same variable is different (-0.041). And th= e Standard Error (0.0043) is over 8 times smaller.

This is obviously extremely important in interpreting the statistical signi= ficance of the results so I'd appreciate any help anyone might be able to o= ffer as to what's going on.

Cheers
Rob


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