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
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- McFadden, Daniel, 1989.
"A Method of Simulated Moments for Estimation of Discrete Response Models without Numerical Integration,"
Econometric Society, vol. 57(5), pages 995-1026, September.
- Daniel McFadden, 1987. "A Method of Simulated Moments for Estimation of Discrete Response Models Without Numerical Integration," Working papers 464, Massachusetts Institute of Technology (MIT), Department of Economics.
- Chib, Siddhartha & Greenberg, Edward, 1996.
"Markov Chain Monte Carlo Simulation Methods in Econometrics,"
Cambridge University Press, vol. 12(03), pages 409-431, August.
- Siddhartha Chib & Edward Greenberg, 1994. "Markov Chain Monte Carlo Simulation Methods in Econometrics," Econometrics 9408001, EconWPA, revised 24 Oct 1994.
- 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.
- John Geweke & Michael Keane & David Runkle, 1994.
"Alternative computational approaches to inference in the multinomial probit model,"
170, Federal Reserve Bank of Minneapolis.
- 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.
- Keane, Michael P, 1994. "A Computationally Practical Simulation Estimator for Panel Data," Econometrica, Econometric Society, vol. 62(1), pages 95-116, January.
- repec:cup:etheor:v:12:y:1996:i:3:p:409-31 is not listed on IDEAS
- 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.
- Lahiri, Kajal & Gao, Jian, 2002.
"Bayesian analysis of nested logit model by Markov chain Monte Carlo,"
Journal of Econometrics,
Elsevier, vol. 111(1), pages 103-133, November.
- Kajal Lahiri & Jian Gao, 2001. "Bayesian Analysis of Nested Logit Model by Markov Chain Monte Carlo," Discussion Papers 01-14, University at Albany, SUNY, Department of Economics.
- Minjung Kyung & Jeff Gill & George Casella, 2011. "Sampling schemes for generalized linear Dirichlet process random effects models," Statistical Methods and Applications, Springer, vol. 20(3), pages 259-290, August.
- Hoshino, Takahiro, 2008. "A Bayesian propensity score adjustment for latent variable modeling and MCMC algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1413-1429, January.
- Daziano, Ricardo A., 2013. "Conditional-logit Bayes estimators for consumer valuation of electric vehicle driving range," Resource and Energy Economics, Elsevier, vol. 35(3), pages 429-450.
- McCulloch, Robert E. & Polson, Nicholas G. & Rossi, Peter E., 2000. "A Bayesian analysis of the multinomial probit model with fully identified parameters," Journal of Econometrics, Elsevier, vol. 99(1), pages 173-193, November.
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