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

Duncan Fong, Sunghoon Kim (), Zhe Chen () and Wayne DeSarbo ()

Psychometrika, 2016, vol. 81, issue 1, 183 pages

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. Copyright The Psychometric Society 2016

Keywords: Bayesian analysis; heterogeneity; multinomial probit model; panel data; parameter expansion; marketing; consumer psychology (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s11336-014-9437-6

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