MCMC Methods for Fitting and Comparing Multinomial Response Models
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.
|Date of creation:||06 Feb 1998|
|Date of revision:||06 May 1998|
|Note:||Type of Document - ps; prepared on TeX; pages: 29 ; figures: included|
|Contact details of provider:|| Web page: http://econwpa.repec.org|
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- John F. Geweke & Michael P. Keane & David E. Runkle, 1994. "Alternative computational approaches to inference in the multinomial probit model," Staff Report 170, Federal Reserve Bank of Minneapolis.
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- Keane, Michael P, 1994. "A Computationally Practical Simulation Estimator for Panel Data," Econometrica, Econometric Society, vol. 62(1), pages 95-116, January.
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