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Bayesian estimation of a random effects heteroscedastic probit model

Author

Listed:
  • Yuanyuan Gu
  • Denzil G. Fiebig
  • Edward Cripps
  • Robert Kohn

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

Suggested Citation

  • Yuanyuan Gu & Denzil G. Fiebig & Edward Cripps & Robert Kohn, 2009. "Bayesian estimation of a random effects heteroscedastic probit model," Econometrics Journal, Royal Economic Society, vol. 12(2), pages 324-339, July.
  • Handle: RePEc:ect:emjrnl:v:12:y:2009:i:2:p:324-339
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    Cited by:

    1. Matteo Richiardi & Ambra Poggi, 2014. "Imputing Individual Effects in Dynamic Microsimulation Models. An application to household formation and labour market participation in Italy," International Journal of Microsimulation, International Microsimulation Association, vol. 7(2), pages 3-39.
    2. Kézdi, Gábor & Mátyás, László & Balázsi, László & Divényi, János Károly, 2014. "A közgazdasági adatforradalom és a panelökonometria
      [The revolution in economic data and panel econometrics]
      ," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(11), pages 1319-1340.

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