There are three reasons results from xtlogit and gllamm do not agree
here for you.
First, your data apparently have a large random effect variance which
creates problems, often, with random effect estimation for logistic
regression. Or it may be simply that you have spare data (e.g.,
little information, small sample size, or few 0 or 1's for the
outcome). The condition code from gllamm you include suggest and the
large differences in parameter estimates suggest something odd with
your data. Most likey the issue is the large random effect  note
rho is about .78 (that is very large for logistic regression).
Second, xtlogit and gllamm use different implementations of adaptive
quadrature. I have run simulations on this and reported to Stata that
xtlogit's adaptive quadrature provides large biased estimates when
large random effects are present and that gllamm estimates and
estimates using adaptive quadrature from SAS are more accurate (and
agree with each other). I hope that with a future update the
implementation in xtlogit changes to agree more with gllamm and SAS.
Note, however, that xtlogit with nonadaptive quadrature works very
well, so try estimating your model with that approach.
Third, despite belief among some that only a few integration points
are needed, when large variances are present in random effects, many
integration points are required. You used only 12; up the number to
150 for gllamm and xtlogit then check estimates. Rerun both with
number of points at 165 or 170 for comparisons to see if number of
points still produces differing results. Run xtlogit with both
adaptive and nonadaptive, and do the same with gllamm and compare results.
Date: Mon, 15 May 2006 14:49:33 0400
From: =?iso88591?Q?B=E9gin_Karine?= <k.begin@umontreal.ca>
Subject: st: xtlogit vs gllamm
Hi,
I have a dichotomous outcome variable taken at various time
points. I performed a regression on this variable using both the
"gllamm" command with a logit link, a binomial distribution and the
adaptative quadrature (ado file) and the "xtlogit" command (running
with the same number of adaptative quadrature points as with
gllamm). Both commands produce quiet different results and I am
wondering why since both gllamm and xtlogit seem to be conceived for
similar purpose. An example of the results is listed below.
Thanks,
Karine
. gllamm emploi sexe FAC3_, i(no_seque) link(logit) family(binom)
nip(12) adapt
Condition Number = 8.8565296


emploi  Coef. Std. Err. z P>z [95%
Conf. Interval]

+
sexe
 2.772054 .3028192 9.15 0.000 3.365569 2.17854
FAC3_
 1.382007 .1186971 11.64 0.000 1.149365 1.614649
_cons
 9.172404 .4245712 21.60 0.000 10.00455 8.34026


Variances and covariances of random effects


***level 2 (no_seque)
var(1): 227.29026 (21.232383)


xtlogit
. xtlogit emploi sexe FAC3_, i(no_seque)


emploi  Coef. Std. Err. z P>z [95%
Conf. Interval]

+
sexe
 .1323433 .1503605 0.88 0.379 .4270445 .1623579
FAC3_
 .2813436 .065331 4.31 0.000 .1532971 .4093901
_cons
 2.290372 .085298 26.85 0.000 2.457553 2.123191

+
/lnsig2u
 2.481279 .0429478 2.397103 2.565455

+
sigma_u
 3.457824 .074253 3.31531 3.606463
rho
 .7842202 .0072676 .7696356 .7981235


Likelihoodratio test of rho=0: chibar2(01) = 7631.32 Prob >=
chibar2 = 0.000
___________________________________________________________________
Bryan W. Griffin
Curriculum, Foundations, & Reading
P.O. Box 8144
Georgia Southern University
Statesboro, GA 304608144
Phone: 9126810488
EMail: bwgriffin@GeorgiaSouthern.edu
WWW: http://coe.georgiasouthern.edu/foundations/bwgriffin/
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