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

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