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

Robert E. McCulloch; Nicholas G. Polson; Peter E. Rossi

doi:10.1016/S0304-4076(00)00034-8

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

Robert E. McCulloch, Nicholas G. Polson, Peter E. Rossi

Research output: Contribution to journal › Article › peer-review

151 Scopus citations

Abstract

We present a new prior and corresponding algorithm for Bayesian analysis of the multinomial probit model. Our new approach places a prior directly on the identified parameter space. The key is the specification of a prior on the covariance matrix so that the (1,1) element if fixed at 1 and it is possible to draw from the posterior using standard distributions. Analytical results are derived which can be used to aid in assessment of the prior.

Original language	English (US)
Pages (from-to)	173-193
Number of pages	21
Journal	Journal of Econometrics
Volume	99
Issue number	1
DOIs	https://doi.org/10.1016/S0304-4076(00)00034-8
State	Published - Nov 2000
Externally published	Yes

Keywords

Bayesian analysis
Priors
Probit models

ASJC Scopus subject areas

Economics and Econometrics

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10.1016/S0304-4076(00)00034-8

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abstract = "We present a new prior and corresponding algorithm for Bayesian analysis of the multinomial probit model. Our new approach places a prior directly on the identified parameter space. The key is the specification of a prior on the covariance matrix so that the (1,1) element if fixed at 1 and it is possible to draw from the posterior using standard distributions. Analytical results are derived which can be used to aid in assessment of the prior.",

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AU - Polson, Nicholas G.

AU - Rossi, Peter E.

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AB - We present a new prior and corresponding algorithm for Bayesian analysis of the multinomial probit model. Our new approach places a prior directly on the identified parameter space. The key is the specification of a prior on the covariance matrix so that the (1,1) element if fixed at 1 and it is possible to draw from the posterior using standard distributions. Analytical results are derived which can be used to aid in assessment of the prior.

KW - Bayesian analysis

KW - Priors

KW - Probit models

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JO - Journal of Econometrics

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ER -

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

Abstract

Keywords

ASJC Scopus subject areas

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