[gov3009-l] Harding on "A Bayesian Mixed Logit-Probit Model for Multinomial Choice"

Justin Grimmer jgrimmer at fas.harvard.edu
Mon Feb 25 19:54:20 EST 2008

Dear Applied Statistics Community,

This Wednesday the Applied Statistics Workshop will welcome Matthew Harding,
Dept. of Economics, Stanford University.  Matthew will be presenting his
research, "A Bayesian Mixed Logit-Probit Model for Multinomial Choice", a
project that is joint with Jerry Hausman and Michael Burda.  Here is an
abstract for the presentation:

In this paper we introduce a new flexible mixed model for multinomial
discrete choice where the key individual- and alternative-specific
parameters of interest are allowed to follow an assumption-free
nonparametric density specification while other alternative-specific
coefficients are assumed to be drawn from a multivariate normal
distribution. A hierarchical specification of our model allows us to break
down a complex data structure into a set of submodels with the desired
features that are naturally assembled in the original system. We estimate
the model using a Bayesian Markov Chain Monte Carlo technique with a
multivariate Dirichlet Process (DP) prior on the coefficients with
nonparametrically estimated density. We bypass a problem of prior
non-conjugacy by employing a "latent class" sampling algorithm for the DP
prior. The model is applied to supermarket choices of a panel of Houston
households whose shopping behavior was observed over a 24-month period in
years 2004-2005. We estimate the nonparametric density of two key variables
of interest: the price of a basket of goods based on scanner data, and
driving distance to the supermarket based on their respective locations,
calculated using GPS software. Supermarket dummies form the parametric part
of our model.

The workshop meets at 12 noon with a light lunch and presentations usually
begin at 1215.  Our workshop is located at 1737 Cambridge St, CGIS-Knafel,
room N-354.

Please contact me with any questions

Justin Grimmer
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