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A Bayesian Multinomial Probit Model for the Analysis of Panel Choice Data

Duncan K. H. FongSunghoon KimZhe ChenWayne S. DeSarbo

出版	SSRN, 2016
URL	http://books.google.com.hk/books?id=DHXhzwEACAAJ&hl=&source=gbs_api

註釋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.