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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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    References listed on IDEAS

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    7. 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.
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    11. 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, June.
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    Cited by:

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    2. 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.
    3. Md Mahfuzur Rahman & Rubayet Karim & Md. Moniruzzaman & Md. Afjal Hossain & Hammad Younes, 2023. "Modeling Hospital Operating Theater Services: A System Dynamics Approach," Logistics, MDPI, vol. 7(4), pages 1-21, November.
    4. 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.
    5. Minke Remmerswaal & Jan Boone, 2020. "A Structural Microsimulation Model for Demand-Side Cost-Sharing in Healthcare," CPB Discussion Paper 415.rdf, CPB Netherlands Bureau for Economic Policy Analysis.
    6. Minke Remmerswaal & Jan Boone, 2020. "A Structural Microsimulation Model for Demand-Side Cost-Sharing in Healthcare," CPB Discussion Paper 415, CPB Netherlands Bureau for Economic Policy Analysis.
    7. Zamiela, Christian & Hossain, Niamat Ullah Ibne & Jaradat, Raed, 2022. "Enablers of resilience in the healthcare supply chain: A case study of U.S healthcare industry during COVID-19 pandemic," Research in Transportation Economics, Elsevier, vol. 93(C).
    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. 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.
    10. 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.
    11. Burda, Martin & Harding, Matthew & Hausman, Jerry, 2012. "A Poisson mixture model of discrete choice," Journal of Econometrics, Elsevier, vol. 166(2), pages 184-203.
    12. 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).
    13. Zheng, Xiaoyong, 2008. "Semiparametric Bayesian estimation of mixed count regression models," Economics Letters, Elsevier, vol. 100(3), pages 435-438, September.
    14. Kevin Dayaratna & Jesse Crosson & Chandler Hubbard, 2022. "Closed Form Bayesian Inferences for Binary Logistic Regression with Applications to American Voter Turnout," Stats, MDPI, vol. 5(4), pages 1-21, November.

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    More about this item

    Keywords

    random efects model; Dirichlet process prior; MCMC;
    All these keywords.

    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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