Bayesian estimation of a random effects heteroscedastic probit model

Yuanyuan Gu*, Denzil G. Fiebig, Edward Cripps, Robert Kohn

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)324-339
Number of pages16
JournalEconometrics Journal
Issue number2
Publication statusPublished - 2009
Externally publishedYes


  • Bayes factor
  • Cross-validation
  • Deviance information criterion
  • Marginal effects
  • Marginal likelihood
  • Markov chain Monte Carlo
  • ROC curve


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