IDEAS home Printed from https://ideas.repec.org/a/ect/emjrnl/v12y2009i2p324-339.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.
    3. E. I. George & V. Ročková & P. R. Rosenbaum & V. A. Satopää & J. H. Silber, 2017. "Mortality Rate Estimation and Standardization for Public Reporting: Medicare’s Hospital Compare," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(519), pages 933-947, July.
    4. J. R. Lockwood & Katherine E. Castellano & Benjamin R. Shear, 2018. "Flexible Bayesian Models for Inferences From Coarsened, Group-Level Achievement Data," Journal of Educational and Behavioral Statistics, , vol. 43(6), pages 663-692, December.
    5. Sean F. Reardon & Benjamin R. Shear & Katherine E. Castellano & Andrew D. Ho, 2017. "Using Heteroskedastic Ordered Probit Models to Recover Moments of Continuous Test Score Distributions From Coarsened Data," Journal of Educational and Behavioral Statistics, , vol. 42(1), pages 3-45, February.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ect:emjrnl:v:12:y:2009:i:2:p:324-339. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley-Blackwell Digital Licensing or Christopher F. Baum (email available below). General contact details of provider: https://edirc.repec.org/data/resssea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.