MCMC Methods for Fitting and Comparing Multinomial Response Models
AbstractThis 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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Bibliographic InfoPaper provided by EconWPA in its series Econometrics with number 9802001.
Length: 29 pages
Date of creation: 06 Feb 1998
Date of revision: 06 May 1998
Note: Type of Document - ps; prepared on TeX; pages: 29 ; figures: included
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Web page: http://220.127.116.11
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 and Methodology: General - - - Bayesian Analysis: General
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
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