IDEAS home Printed from https://ideas.repec.org/p/kse/dpaper/41.html
   My bibliography  Save this paper

A Bayesian Model of Sample Selection with a Discrete Outcome Variable: Detecting Depression in Older Adults

Author

Listed:
  • Maksym Obrizan

    () (Kyiv School of Economics, Kyiv Economic Institute)

Abstract

Depression as a major mental illness among older adults has attracted a lot of research attention. However, the problem of sample selection, inevitable in most health surveys, has been largely ignored. To fill in this gap, this paper formally models selection into the sample jointly with a discrete outcome variable for depression. A Bayesian model of sample selection is developed from a multivariate probit by (i) allowing missing depression status for nonselected respondents, and (ii) using Cholesky factorization of the inverse variance matrix to avoid a Metropolis-Hastings step in the Gibbs sampler. Non-selected respondents are less likely to suffer from depression.

Suggested Citation

  • Maksym Obrizan, 2011. "A Bayesian Model of Sample Selection with a Discrete Outcome Variable: Detecting Depression in Older Adults," Discussion Papers 41, Kyiv School of Economics.
  • Handle: RePEc:kse:dpaper:41 Note: Journal of Applied Econometrics
    as

    Download full text from publisher

    File URL: http://repec.kse.org.ua/pdf/KSE_dp41.pdf
    File Function: July 2011
    Download Restriction: no

    References listed on IDEAS

    as
    1. Meng, Chun-Lo & Schmidt, Peter, 1985. "On the Cost of Partial Observability in the Bivariate Probit Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 26(1), pages 71-85, February.
    2. Ho-Chuan Huang, 2001. "Bayesian analysis of the SUR Tobit model," Applied Economics Letters, Taylor & Francis Journals, vol. 8(9), pages 617-622.
    3. Munkin, Murat K. & Trivedi, Pravin K., 2003. "Bayesian analysis of a self-selection model with multiple outcomes using simulation-based estimation: an application to the demand for healthcare," Journal of Econometrics, Elsevier, pages 197-220.
    4. John Geweke, 2004. "Getting It Right: Joint Distribution Tests of Posterior Simulators," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 799-804, January.
    5. F. Thomas Juster & Richard Suzman, 1995. " An Overview of the Health and Retirement Study," Journal of Human Resources, University of Wisconsin Press, vol. 30, pages s7-s56.
    6. Van de Ven, Wynand P. M. M. & Van Praag, Bernard M. S., 1981. "The demand for deductibles in private health insurance : A probit model with sample selection," Journal of Econometrics, Elsevier, pages 229-252.
    7. Donald S. Kenkel & Joseph V. Terza, 2001. "The effect of physician advice on alcohol consumption: count regression with an endogenous treatment effect," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(2), pages 165-184.
    8. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Publishing House "SINERGIA PRESS", pages 129-137.
    9. Chib, Siddhartha & Hamilton, Barton H., 2000. "Bayesian analysis of cross-section and clustered data treatment models," Journal of Econometrics, Elsevier, pages 25-50.
    10. Boyes, William J. & Hoffman, Dennis L. & Low, Stuart A., 1989. "An econometric analysis of the bank credit scoring problem," Journal of Econometrics, Elsevier, pages 3-14.
    11. Madhu Mohanty, 2002. "A bivariate probit approach to the determination of employment: a study of teen employment differentials in Los Angeles County," Applied Economics, Taylor & Francis Journals, vol. 34(2), pages 143-156.
    12. Kai, Li, 1998. "Bayesian inference in a simultaneous equation model with limited dependent variables," Journal of Econometrics, Elsevier, pages 387-400.
    13. Chib, Siddhartha & Hamilton, Barton H., 2002. "Semiparametric Bayes analysis of longitudinal data treatment models," Journal of Econometrics, Elsevier, pages 67-89.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Multivariate probit model; Sample selection; Bayesian methods; Gibbs sampler;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • I1 - Health, Education, and Welfare - - Health

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:kse:dpaper:41. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Iryna Sobetska). General contact details of provider: http://edirc.repec.org/data/ksecoua.html .

    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.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.