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Copula bivariate probit models: with an application to medical expenditures

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  • Rainer Winkelmann

Abstract

The bivariate probit model is frequently used for estimating the effect of an endogenous binary regressor (the "treatment") on a binary health outcome variable. This paper discusses simple modifications that maintain the probit assumption for the marginal distributions while introducing non-normal dependence using copulas. In an application of the copula bivariate probit model to the effect of insurance status on the absence of ambulatory health care expen- diture, a model based on the Frank copula outperforms the standard bivariate probit model.

Suggested Citation

  • Rainer Winkelmann, 2011. "Copula bivariate probit models: with an application to medical expenditures," ECON - Working Papers 029, Department of Economics - University of Zurich.
    • Handle: RePEc:zur:econwp:029
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    Citations

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    Cited by:

    1. Keay, Myoung-Jin, 2016. "Partial copula methods for models with multiple discrete endogenous explanatory variables and sample selection," Economics Letters, Elsevier, vol. 144(C), pages 85-87.
    2. Ziebarth, Nicolas R., 2017. "Social Insurance and Health," IZA Discussion Papers 10918, Institute for the Study of Labor (IZA).
    3. Jörg Schwiebert, 2016. "Multinomial choice models based on Archimedean copulas," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 100(3), pages 333-354, July.
    4. Pravin Trivedi & David Zimmer, 2017. "A Note on Identification of Bivariate Copulas for Discrete Count Data," Econometrics, MDPI, Open Access Journal, vol. 5(1), pages 1-11, February.
    5. Magdalena Six & Franz Wirl & Jaqueline Wolf, 2017. "Information as potential key determinant in switching electricity suppliers," Journal of Business Economics, Springer, vol. 87(2), pages 263-290, February.
    6. repec:eee:ecmode:v:76:y:2019:i:c:p:305-318 is not listed on IDEAS
    7. Hasebe, Takuya, 2013. "Marginal effects of a bivariate binary choice model," Economics Letters, Elsevier, vol. 121(2), pages 298-301.
    8. repec:eee:transb:v:108:y:2018:i:c:p:84-105 is not listed on IDEAS
    9. Manner, Hans & Türk, Dennis & Eichler, Michael, 2016. "Modeling and forecasting multivariate electricity price spikes," Energy Economics, Elsevier, vol. 60(C), pages 255-265.
    10. repec:eee:ecolet:v:171:y:2018:i:c:p:239-244 is not listed on IDEAS
    11. Katarzyna Bień-Barkowska, 2012. "A Bivariate Copula-based Model for a Mixed Binary-Continuous Distribution: A Time Series Approach," Central European Journal of Economic Modelling and Econometrics, CEJEME, vol. 4(2), pages 117-142, June.
    12. Mark McGovern & David Canning & Till Bärnighausen, 2018. "Accounting for Non-Response Bias using Participation Incentives and Survey Design," CHaRMS Working Papers 18-02, Centre for HeAlth Research at the Management School (CHaRMS).
    13. repec:eee:econom:v:199:y:2017:i:1:p:63-73 is not listed on IDEAS
    14. Giampiero Marra & Rosalba Radice & Silvia Missiroli, 2014. "Testing the hypothesis of absence of unobserved confounding in semiparametric bivariate probit models," Computational Statistics, Springer, vol. 29(3), pages 715-741, June.

    More about this item

    Keywords

    Bivariate probit; binary endogenous regressor; Frank copula; Clayton copula;

    JEL classification:

    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior

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