Ken Train has Gauss code for estimating a
nested logit model using full information maximum likelihood on his UC Berkeley
webpage (http://elsa.berkeley.edu/~train/ps.html).
I believe the nested logit code is in problem set #3. This should not be too
hard to program in S-PLUS. As part of a discrete choice estimation library
under development at Insightful, there is a nested logit routine based on full
information maximum likelihood that has good numerical properties (it is similar
to but more general than Ken Train’s Gauss code) in which the estimation
is done using nlminb. In general, nlminb will handle most problems that can be
estimated using the maxlik routine in GAUSS. For more estimation options, you
might also look at the function optmin available in the MASS library (which
come with S-PLUS) provided by Venables and Ripley.
BTW, if you are interested in
probabilistic discrete choice model, I highly recommend doing Ken Train
distance learning course on discrete choice models with simulation (and reading
his new book discrete choice models with simulation). The web page for this is
http://elsa.berkeley.edu/~train/distant.html
ez
-----Original Message-----
From:
s-news-owner@lists.biostat.wustl.edu
[mailto:s-news-owner@lists.biostat.wustl.edu] On
Behalf Of Adrienne Kandel
Sent: Wednesday,
August 27, 2003 2:28 PM
To: s-news@lists.biostat.wustl.edu
Subject: [S] Simultaneously
estimated nested logit
I'm learning S+, and I'd like
to re-program some work I initially did using another package (Gauss).
One piece of that work requires a three choice nested logit regression (with 2
choices forming one branch and the other choice forming the other branch).
1. Does someone know of an S
program that does this estimation simultaneously (not sequentially), with good
numerical properties. I wrote one in Gauss and can translate it into S,
but if I can avoid the big learning curve I'll be happy. (I took the
inclusive value as given, and used Gauss's maximum likelihood routine to find
the optimum. I did this as a grid search over inclusive values, getting
to finer and finer grid levels as I reached the optimum, so that I ended up
with the inclusive value that yielded the max of the max likelihoods, and the
corresponding parameters and other model output.)
2. I hear S+ has a maximum
likelihood routine with some flexibility to choose solving methods. Any
tips on it?