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Estimating the demand for health care with panel data: a semiparametric Bayesian approach

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  • Markus Jochmann
  • Roberto León‐González

Abstract

This paper is concerned with the problem of estimating the demand for health care with panel data. A random effects model is specified within a semiparametric Bayesian approach using a Dirichlet process prior. This results in a very flexible distribution for both the random effects and the count variable. In particular, the model can be seen as a mixture distribution with a random number of components, and is therefore a natural extension of prevailing latent class models. A full Bayesian analysis using Markov chain Monte Carlo simulation methods is proposed. The methodology is illustrated with an application using data from Germany. Copyright © 2004 John Wiley & Sons, Ltd.

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  • Markus Jochmann & Roberto León‐González, 2004. "Estimating the demand for health care with panel data: a semiparametric Bayesian approach," Health Economics, John Wiley & Sons, Ltd., vol. 13(10), pages 1003-1014, October.
    • Handle: RePEc:wly:hlthec:v:13:y:2004:i:10:p:1003-1014
    DOI: 10.1002/hec.936
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    Cited by:

    1. 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.
    2. 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).
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    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.
    11. 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).
    12. Zheng, Xiaoyong, 2008. "Semiparametric Bayesian estimation of mixed count regression models," Economics Letters, Elsevier, vol. 100(3), pages 435-438, September.
    13. 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.
    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

    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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