title: A Bayesian Multinomial Probit MODEL FOR THE ANALYSIS OF PANEL CHOICE DATA.
abstract: A new Bayesian multinomial probit model is proposed for the analysis of panel choice data. Using a parameter expansion technique, we are able to devise a Markov Chain Monte Carlo algorithm to compute our Bayesian estimates efficiently. We also show that the proposed procedure enables the estimation of individual level coefficients for the single-period multinomial probit model even when the available prior information is vague. We apply our new procedure to consumer purchase data and reanalyze a well-known scanner panel dataset that reveals new substantive insights. In addition, we delineate a number of advantageous features of our proposed procedure over several benchmark models. Finally, through a simulation analysis employing a fractional factorial design, we demonstrate that the results from our proposed model are quite robust with respect to differing factors across various conditions.
authors.affiliation: Smeal College of Business, The Pennsylvania State University, University Park, PA, 16802 , USA.
authors.forename: Duncan K H
authors.initials: DK
authors.lastname: Fong
authors.affiliation: W.P. Carey School of Business, Arizona State University, Tempe, AZ, 85258, USA. hoon.mkt@gmail.com.
authors.forename: Sunghoon
authors.initials: S
authors.lastname: Kim
authors.affiliation: Google Inc., Mountain View, CA, USA. zhec@google.com.
authors.forename: Zhe
authors.initials: Z
authors.lastname: Chen
authors.affiliation: The Pennsylvania State University, University Park, PA, 16802 , USA. wsd6@psu.edu.
authors.forename: Wayne S
authors.initials: WS
authors.lastname: DeSarbo
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D003198:Computer Simulation; D006801:Humans; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D011594:Psychometrics
keywords: Bayesian analysis; consumer psychology; heterogeneity; marketing; multinomial probit model; panel data; parameter expansion