st: multinomial logit using -gllamm-

[<S.Jenkins@lse.ac.uk>]

------------------------------

Date: Tue, 08 Mar 2011 09:19:30 -0500
From: Jeph Herrin <stata@spandrel.net>
Subject: st: multinomial logit using -gllamm-

This question may be better directed to the authors of -gllamm-, but I
thought I should
start with the list before bothering them.

I want to estimate random effects multinomial logit models, and one
solution in Stata
is to use -gllamm-. However, there the help file and the -gllamm- manual
differ in
describing how to do this, and the results differ.

In the help file, I would estimate an empty model like this:

   gllamm depvar , i(groupvar) link(mlogit) family(binomial)

and this model does in fact converge, producing a single variance for a
single random effect. However, since I have three intercepts (-depvar-
takes 4 values), I went to the -gllamm- manual to understand why, and
found there that this model should be estimated by expanding the data to
4 obs per original measurement, with a new variable -alt- indicating
which
obs is the actual outcome, then

  gllamm alt , expand(obsid depvar m) i(groupvar) link(mlogit)
fam(binomial) ///
              nrf(3) eqs(a1 a2 a3)

this model also converges, and produces 3 random effect variances, one
for
each intercept, which makes sense.

However, the first model produces intercepts that look very much like I
would
expect from -mlogit-, whereas the intercepts from the second model are
of different
magnitudes and in one case direction, so it seems very suspect.

My question: what is the first model estimating for a random variance,
and why does
the second one produce such different results for the fixed effects?

thanks,
Jeph

=========

Not an answer to your specific question, but you might also consider
estimation using simulated likelihood. See:

Peter Haan and Arne Uhlendorff "Estimation of multinomial logit models
with unobserved heterogeneity using maximum simulated likelihood", The
Stata Journal (2006) 6, Number 2, pp. 229-245

Downloadable from SJ website. I recall they use Normal random effects,
however, rather than discrete mass points.

Stephen
------------------
Stephen P. Jenkins <s.jenkins@lse.ac.uk>
Department of Social Policy and STICERD
London School of Economics and Political Science
Houghton Street
London WC2A 2AE
United Kingdom
Tel: +44(0)20 7955 6527
Survival Analysis Using Stata:
http://www.iser.essex.ac.uk/survival-analysis
Downloadable papers and software: http://ideas.repec.org/e/pje7.html



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