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

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  • Duncan Fong
  • Sunghoon Kim
  • Zhe Chen
  • Wayne DeSarbo

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

Suggested Citation

  • Duncan Fong & Sunghoon Kim & Zhe Chen & Wayne DeSarbo, 2016. "A Bayesian Multinomial Probit MODEL FOR THE ANALYSIS OF PANEL CHOICE DATA," Psychometrika, Springer;The Psychometric Society, vol. 81(1), pages 161-183, March.
    • Handle: RePEc:spr:psycho:v:81:y:2016:i:1:p:161-183
    DOI: 10.1007/s11336-014-9437-6
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