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

Author

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  • 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

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    File URL: http://repec.kse.org.ua/pdf/KSE_dp41.pdf
    File Function: July 2011
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Multivariate probit model; Sample selection; Bayesian methods; Gibbs sampler;
    All these keywords.

    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

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