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A High-dimensional Multinomial Choice Model

Didier Nibbering ()

No 19/19, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: The number of parameters in a standard multinomial choice model increases linearly with the number of choice alternatives and number of explanatory variables. Since many modern applications involve large choice sets with categorical explanatory variables, which enter the model as large sets of binary dummies, the number of parameters easily approaches the sample size. This paper proposes a new method for data-driven parameter clustering over outcome categories and explanatory dummy categories in a multinomial probit setting. A Dirichlet process mixture encourages parameters to cluster over the categories, which favours a parsimonious model specification without a priori imposing model restrictions. An application to a dataset of holiday destinations shows a decrease in parameter uncertainty, an enhancement of the parameter interpretability, and an increase in predictive performance, relative to a standard multinomial choice model.

Keywords: large choice sets; Dirichlet process prior; multinomial probit model; high-dimensional models (search for similar items in EconPapers)
JEL-codes: C11 C14 C25 C35 C51 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-dcm, nep-ecm and nep-ore
Date: 2019
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