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Estimating the Demand for Health Care with Panel Data: A Semiparametric Bayesian Approach

Author

Listed:
  • Markus Jochmann
  • Roberto Leon-Gonzalez

    () (Department of Economics, The University of Sheffield)

Abstract

This paper is concerned with the problem of estimating the demand for health care with panel data. A random effects model is specifed in a semiparametric Bayesian fashion using a Dirichlet process prior. This results in a very exible mixture distribution with an in nite number of components for the random effects. Therefore, the model can be seen as a natural extension of prevailing latent class models. A full Bayesian analysis using Markov chain Monte Carlo (MCMC)simulation methods is discussed. The methodology is illustrated with an application using data from Germany.

Suggested Citation

  • Markus Jochmann & Roberto Leon-Gonzalez, 2003. "Estimating the Demand for Health Care with Panel Data: A Semiparametric Bayesian Approach," Working Papers 2003005, The University of Sheffield, Department of Economics, revised Oct 2003.
  • Handle: RePEc:shf:wpaper:2003005
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    File URL: http://www.shef.ac.uk/content/1/c6/06/31/66/SERP2003005.pdf
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    File URL: http://www.shef.ac.uk/content/1/c6/06/31/66/SERP2003005.pdf
    File Function: Revised version, 2003
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    References listed on IDEAS

    as
    1. Angel López-Nicolás, 1998. "Unobserved heterogeneity and censoring in the demand for health care," Health Economics, John Wiley & Sons, Ltd., vol. 7(5), pages 429-437.
    2. Andreas Million & Regina T. Riphahn & Achim Wambach, 2003. "Incentive effects in the demand for health care: a bivariate panel count data estimation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(4), pages 387-405.
    3. Deb, Partha & Trivedi, Pravin K., 2002. "The structure of demand for health care: latent class versus two-part models," Journal of Health Economics, Elsevier, vol. 21(4), pages 601-625, July.
    4. Gurmu, Shiferaw, 1997. "Semi-Parametric Estimation of Hurdle Regression Models with an Application to Medicaid Utilization," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(3), pages 225-243, May-June.
    5. Chib, Siddhartha & Winkelmann, Rainer, 2001. "Markov Chain Monte Carlo Analysis of Correlated Count Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(4), pages 428-435, October.
    6. Chib, Siddhartha & Hamilton, Barton H., 2002. "Semiparametric Bayes analysis of longitudinal data treatment models," Journal of Econometrics, Elsevier, vol. 110(1), pages 67-89, September.
    7. Deb, Partha & Trivedi, Pravin K, 1997. "Demand for Medical Care by the Elderly: A Finite Mixture Approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(3), pages 313-336, May-June.
    8. Elke Holst & Dean R. Lillard & Thomas A. DiPrete, 2001. "Proceedings of the 2000 Fourth International Conference of German Socio-Economic Panel Study Users (GSOEP 2000): Editorial Introduction," Vierteljahrshefte zur Wirtschaftsforschung / Quarterly Journal of Economic Research, DIW Berlin, German Institute for Economic Research, vol. 70(1), pages 5-6.
    9. Sergi Jiménez-Martín & José M. Labeaga & Maite Martínez-Granado, 2002. "Latent class versus two-part models in the demand for physician services across the European Union," Health Economics, John Wiley & Sons, Ltd., vol. 11(4), pages 301-321.
    10. Winfried Pohlmeier & Volker Ulrich, 1995. "An Econometric Model of the Two-Part Decisionmaking Process in the Demand for Health Care," Journal of Human Resources, University of Wisconsin Press, vol. 30(2), pages 339-361.
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    Citations

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    Cited by:

    1. Markus Jochmann, 2013. "What belongs where? Variable selection for zero-inflated count models with an application to the demand for health care," Computational Statistics, Springer, vol. 28(5), pages 1947-1964, October.
    2. Jing Dai & Stefan Sperlich & Walter Zucchini, 2011. "Estimating and predicting the distribution of the number of visits to the medical doctor," MAGKS Papers on Economics 201148, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    3. Ketelhöhn, Niels & Sanz, Luis, 2016. "Healthcare management priorities in Latin America: Framework and responses," Journal of Business Research, Elsevier, vol. 69(9), pages 3835-3838.
    4. Davis, Alison F. & Moeltner, Klaus, 2010. "Valuing the Prevention of an Infestation: The Threat of the New Zealand Mud Snail in Northern Nevada," Agricultural and Resource Economics Review, Northeastern Agricultural and Resource Economics Association, vol. 39(1), February.
    5. Thomas Brenner & Claudia Werker, 2007. "A Taxonomy of Inference in Simulation Models," Computational Economics, Springer;Society for Computational Economics, vol. 30(3), pages 227-244, October.
    6. Burda, Martin & Harding, Matthew & Hausman, Jerry, 2008. "A Bayesian mixed logit-probit model for multinomial choice," Journal of Econometrics, Elsevier, vol. 147(2), pages 232-246, December.
    7. Zheng, Xiaoyong, 2008. "Semiparametric Bayesian estimation of mixed count regression models," Economics Letters, Elsevier, vol. 100(3), pages 435-438, September.
    8. Arnab Mukherji & Satrajit Roychowdhury & Pulak Ghosh & Sarah Brown, 2012. "Estimating Healthcare Demand for an Aging Population: A Flexible and Robust Bayesian Joint Model," Working Papers 2012027, The University of Sheffield, Department of Economics.
    9. Buddhavarapu, Prasad & Scott, James G. & Prozzi, Jorge A., 2016. "Modeling unobserved heterogeneity using finite mixture random parameters for spatially correlated discrete count data," Transportation Research Part B: Methodological, Elsevier, vol. 91(C), pages 492-510.
    10. Burda, Martin & Harding, Matthew & Hausman, Jerry, 2012. "A Poisson mixture model of discrete choice," Journal of Econometrics, Elsevier, vol. 166(2), pages 184-203.

    More about this item

    Keywords

    random efects model; Dirichlet process prior; MCMC;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • I10 - Health, Education, and Welfare - - Health - - - General

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