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Bayesian estimation of a random effects heteroscedastic probit model

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Author Info
Yuanyuan Gu
Denzil G. Fiebig
Edward Cripps
Robert Kohn

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Abstract

Bayesian analysis is given of a random effects binary probit model that allows for heteroscedasticity. Real and simulated examples illustrate the approach and show that ignoring heteroscedasticity when it exists may lead to biased estimates and poor prediction. The computation is carried out by an efficient Markov chain Monte Carlo sampling scheme that generates the parameters in blocks. We use the Bayes factor, cross-validation of the predictive density, the deviance information criterion and Receiver Operating Characteristic (ROC) curves for model comparison. Copyright © 2009 The Author(s). Journal compilation © Royal Economic Society 2009

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File URL: http://www.blackwell-synergy.com/doi/abs/10.1111/j.1368-423X.2009.00283.x
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Publisher Info
Article provided by Royal Economic Society in its journal Econometrics Journal.

Volume (Year): 12 (2009)
Issue (Month): 2 (07)
Pages: 324-339
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Handle: RePEc:ect:emjrnl:v:12:y:2009:i:2:p:324-339

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This page was last updated on 2009-11-27.

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