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

Duncan K H Fong1, Sunghoon Kim2, Zhe Chen3

  • 1Smeal College of Business, The Pennsylvania State University, University Park, PA, 16802 , USA.

Psychometrika
|December 11, 2014
PubMed
Summary
This summary is machine-generated.

A new Bayesian multinomial probit model efficiently analyzes panel choice data using Markov Chain Monte Carlo. This robust method estimates individual coefficients even with vague prior information, offering new insights into consumer behavior.

Keywords:
Bayesian analysisconsumer psychologyheterogeneitymarketingmultinomial probit modelpanel dataparameter expansion

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Area of Science:

  • Econometrics
  • Statistical Modeling
  • Consumer Behavior Analysis

Background:

  • Panel choice data analysis presents challenges in estimating individual-level coefficients.
  • Existing models may struggle with vague prior information and computational efficiency.

Purpose of the Study:

  • To introduce a novel Bayesian multinomial probit model for panel choice data.
  • To develop an efficient Markov Chain Monte Carlo (MCMC) algorithm for Bayesian estimation.
  • To enable the estimation of individual-level coefficients in single-period multinomial probit models.

Main Methods:

  • Development of a new Bayesian multinomial probit model.
  • Application of a parameter expansion technique for efficient MCMC algorithm design.
  • Estimation of individual-level coefficients using vague prior information.

Main Results:

  • The proposed model efficiently computes Bayesian estimates via MCMC.
  • Individual-level coefficients can be estimated even with limited prior information.
  • Application to consumer purchase data yielded new substantive insights.

Conclusions:

  • The new Bayesian multinomial probit model offers an efficient and robust approach for panel choice data analysis.
  • The method provides advantages over benchmark models and demonstrates robustness in simulations.
  • It enhances the understanding of consumer purchasing decisions through individual-level coefficient estimation.