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MCMC Methods for Fitting and Comparing Multinomial Response Models

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Author Info
Siddhartha Chib (Washington University)
Edward Greenberg (Washington University)
Yuxin Chen (Washington University)
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.

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Publisher Info
Paper provided by EconWPA in its series Econometrics with number 9802001.

Download reference. The following formats are available: HTML (with abstract), plain text (with abstract), BibTeX, RIS (EndNote, RefMan, ProCite), ReDIF
Length: 29 pages
Date of creation: 06 Feb 1998
Date of revision: 06 May 1998
Handle: RePEc:wpa:wuwpem:9802001

Note: Type of Document - ps; prepared on TeX; pages: 29 ; figures: included
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Web page: http://129.3.20.41

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Related research
Keywords: Bayes factor; Gibbs sampling; Monte Carlo EM algorithm; Marginal likelihood; Metropolis-Hastings algorithm; Multinomial logit; Multinomial probit; Multinomial-t; Model comparison.;

Find related papers by JEL classification:
C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Bayesian Analysis
C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Statistical Simulation Methods
C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models

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  1. McFadden, Daniel, 1989. "A Method of Simulated Moments for Estimation of Discrete Response Models without Numerical Integration," Econometrica, Econometric Society, vol. 57(5), pages 995-1026, September. [Downloadable!] (restricted)
    Other versions:
  2. Keane, Michael P, 1992. "A Note on Identification in the Multinomial Probit Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(2), pages 193-200, April.
  3. Keane, Michael P, 1994. "A Computationally Practical Simulation Estimator for Panel Data," Econometrica, Econometric Society, vol. 62(1), pages 95-116, January. [Downloadable!] (restricted)
  4. McCulloch, Robert & Rossi, Peter E., 1994. "An exact likelihood analysis of the multinomial probit model," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 207-240. [Downloadable!] (restricted)
  5. Chib, Siddhartha & Greenberg, Edward, 1996. "Markov Chain Monte Carlo Simulation Methods in Econometrics," Econometric Theory, Cambridge University Press, vol. 12(03), pages 409-431, August. [Downloadable!]
  6. Geweke, John & Keane, Michael P & Runkle, David, 1994. "Alternative Computational Approaches to Inference in the Multinomial Probit Model," The Review of Economics and Statistics, MIT Press, vol. 76(4), pages 609-32, November. [Downloadable!] (restricted)
    Other versions:
  7. repec:cup:etheor:v:12:y:1996:i:3:p:409-31 is not listed on IDEAS
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