A Bayesian Multinomial Probit Model for the Analysis of Panel Choice Data
Psychometrika, Volume 81, Issue 1, pp 161–183, 2016
Posted: 18 Jun 2016
Date Written: March 2016
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.
Keywords: Bayesian analysis, heterogeneity, multinomial probit model, panel data, parameter expansion, marketing, consumer psychology
Suggested Citation: Suggested Citation