A Bayesian analysis of the multinomial probit model with fully identified parameters

Journal of Econometrics

Volumn 99, Issue 1, 2000, Pages 173-193

  McCulloch, Robert E ^a   Polson, Nicholas G ^a   Rossi, Peter E ^a  

^a UNIVERSITY OF CHICAGO   (United States)

Author keywords

Bayesian analysis; Priors; Probit models

Indexed keywords

EID: 0001434493     PISSN: 03044076     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0304-4076(00)00034-8     Document Type: Article

References (29)

1. Ainslie, A., 1998. Similarities and differences in brand purchase behavior across categories. Unpublished dissertation, University of Chicago.
2. Albert, J., Chib, S., 1993. Bayesian analysis of binary and polychotomous data. Journal of the American Statistical Association 88, 669-679.
3. Barnard, J., Meng, X., McCulloch, R., 2000. Modeling covariance matrices in terms of standard deviations and correlations, with application to shrinkage. Statistica Sinica, forthcoming.
4. Borsch-Supan, A., Hajivassiliou, V., 1993. Smooth unbiased multivariate probability simulators for maximum likelihood estimation of limited dependent variable models. Journal of Econometrics 58, 347-368.
5. Bunch, D.S., 1991. Estimability in the multinomial probit model. Transportation Research-B 25B, 1-12.
6. Casella, G., George, E.I., 1992. Explaining the Gibbs sampler. The American Statistician 46, 167-174.
7. Chib, S., Greenberg, E., Chen, Y., 1998. MCMC methods for fitting and comparing multinomial response models. Working Paper, Olin School of Business, Washington University.
8. Dansie, B., 1985. Parameter estimability in the multinomial probit model. Transportation Research-B 19B:6, 526-28.
9. Daniels, M.J., Kass, R.E., 1999. Nonconjugate bayesian estimation of covariance matrices and its use in hierarchical models. Journal of the American Statistical Association 94, 1254-1263.
10. Gelfand, A.E., Sahu, S.K., 1999. Identifiability, improper priors, and Gibbs sampling for generalized linear models. Journal of the American Statistical Association 94, 247-253.
11. Gelfand, A., Smith, A.F.M., 1990. Sampling based approaches to calculating marginal densities. Journal of the American Statistical Association 85, 972-985.
12. Hajivassiliou, V., McFadden, D., 1990. The method of simulated scores for the estimation of LDV models with an application to external debt crises. Working paper, Yale University.
13. Jacquier, E., Polson, N., Rossi, P.E., 1994. Stochastic volatility: univariate and multivariate extensions. Working paper, University of Chicago.
14. Keane, M., 1992. A note on identification in the multinomial probit model. Journal of Business and Economics Statistics 10, 193-200.
15. Keane, M., 1994. A computationally practical simulation estimator for panel data. Econometrica 62, 95-116.
16. Koop, G., Poirier, D., 1993. Bayesian analysis of logit models using natural conjugate priors. Journal of Econometrics 56, 323-340.
17. Koop, G., Poirier, D., 1997. Learning about the across-regime correlation in swithcing regression models. Journal of Econometrics 78, 217-228.
18. Li, K., 1996. Essays in Bayesian financial econometrics. Ph.D. Thesis, Department of Economics, University of Toronto.
19. McCulloch, R., Polson, N.G., Rossi, P.E., 1994. A Bayesian analysis of the multinomial probit model with fully identified parameters. Working paper, Graduate School of Business, University of Chicago.
20. McCulloch, R., Rossi, P.E., 1994. An exact likelihood analysis of the multinomial probit model. Journal of Econometrics 64, 207-240.
21. McCulloch, R., Rossi, P.E., 1996. Bayesian analysis of the multinomial probit model. In: R. Mariano, Weeks, T. Schuermann (Eds.), Simulation-Based Inference in Econometrics, Cambridge University Press, Cambridge.
22. McFadden, D., 1989. A method of simulated moments for estimation of discrete response models without numerical integration. Econometrica 57, 995-1027.
23. Nevo, A., 1997. Measuring market power in the ready-to-eat cereal industry. Working paper, University of California, Berkeley.
24. Nobile, A., 1998. A hybrid markov chain for the bayesian analysis of the multinomial probit model. Statistics and Computing 8, 229-242.
25. Poirier, D., 1998. Revising beliefs in nonidentifed models. Econometric Theory 14, 483-509.
26. Rossi, P.E., Allinby, G., McCulloch, R., 1996. On the value of household information in target marketing. Marketing Science 15, 321-340.
27. Smith, A.F.M., Roberts, G.O., 1993. Bayesian computation via the Gibbs sampler and related Markov chain Monte Carlo methods. Journal of the Royal Statistical Society. Series B 55, 3-25.
28. Tierney, L., 1994. Markov chains for exploring posterior distributions. Annals of Statistics 22, 1701-1762.
29. Yang, R., Berger, J.O., 1994. Estimation of a covariance matrix using the reference prior. Annals of Statistics 22, 1195-1211.