Bayesian estimation of a random effects heteroscedastic probit model

Yuanyuan Gu, D. Fiebig, Edward Cripps, R. Kohn

    Research output: Contribution to journalArticlepeer-review

    7 Citations (Web of Science)


    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
    JournalEconometrics Journal
    Issue number2
    Publication statusPublished - 2009


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