A Bayesian Multinomial Probit MODEL FOR THE ANALYSIS OF PANEL CHOICE DATA

Duncan K H Fong, Sunghoon Kim, Zhe Chen, Wayne S. DeSarbo

Research output: Contribution to journalArticle

5 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)161-183
Number of pages23
JournalPsychometrika
Volume81
Issue number1
DOIs
StatePublished - Mar 1 2016

Keywords

  • Bayesian analysis
  • consumer psychology
  • heterogeneity
  • marketing
  • multinomial probit model
  • panel data
  • parameter expansion

ASJC Scopus subject areas

  • Psychology(all)
  • Applied Mathematics

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