MCMC Methods for Fitting and Comparing Multinomial Response Models

Siddhartha Chib, Edward Greenberg and Yuxin Chen
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Siddhartha Chib: Washington University
Edward Greenberg: Washington University
Yuxin Chen: Washington University

Econometrics from EconWPA

Abstract: This paper is concerned with statistical inference in multinomial probit, multinomial-$t$ and multinomial logit models. New Markov chain Monte Carlo (MCMC) algorithms for fitting these models are introduced and compared with existing MCMC methods. The question of parameter identification in the multinomial probit model is readdressed. Model comparison issues are also discussed and the method of Chib (1995) is utilized to find Bayes factors for competing multinomial probit and multinomial logit models. The methods and ideas are illustrated in detail with an example.

Keywords: Bayes factor; Gibbs sampling; Monte Carlo EM algorithm; Marginal likelihood; Metropolis-Hastings algorithm; Multinomial logit; Multinomial probit; Multinomial-t; Model comparison. (search for similar items in EconPapers)
JEL-codes: C11 C15 C25 (search for similar items in EconPapers)
Date: 1998-02-06, Revised 1998-05-06
Note: Type of Document - ps; prepared on TeX; pages: 29 ; figures: included
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Persistent link: http://EconPapers.repec.org/RePEc:wpa:wuwpem:9802001

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