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| Title: | | A Bayesian mixed logit-probit model for multinomial choice  |
| Authors: | | Burda, Martin Harding, Matthew Hausman, Jerry |
| Issue Date: | | 2008 |
| Series/Report no.: | | cemmap working paper CWP23/08 |
| Abstract: | | 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 assumptionfree nonparametric density specification while other alternative-specific coefficients are assumed to be drawn from a multivariate normal distribution which eliminates the independence of irrelevant alternatives assumption at the individual level. 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 employ a 'latent class' sampling algorithm which is applicable to a general class of models including non-conjugate DP base priors. 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. Our semi-parametric approach allows us to identify a complex multi-modal preference distribution which distinguishes between inframarginal consumers and consumers who strongly value either lower prices or shopping convenience. |
| Subjects: | | multinomial discrete choice model Dirichlet Process prior non-conjugate priors hierarchical latent class models |
| JEL: | | C11 C13 C14 C15 C23 C25 |
| Persistent Identifier of the first edition: | | doi:10.1920/wp.cem.2008.2308 |
| Document Type: | | Working Paper |
| Appears in Collections: | | cemmap working papers, Centre for Microdata Methods and Practice, Institute for Fiscal Studies (IFS)
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