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

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Author Info

  • Markus Jochmann

    (Department of Economics, University of Konstanz, Germany)

  • Roberto León-González

    (Department of Economics, University of Sheffield, UK)

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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File URL: http://hdl.handle.net/10.1002/hec.936
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Bibliographic Info

Article provided by John Wiley & Sons, Ltd. in its journal Health Economics.

Volume (Year): 13 (2004)
Issue (Month): 10 ()
Pages: 1003-1014

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Handle: RePEc:wly:hlthec:v:13:y:2004:i:10:p:1003-1014

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Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/5749

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References

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  1. 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 313-36, May-June.
  2. 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.
  3. 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.
  4. 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.
  5. Sergi Jiménez-Mart�n & José M. Labeaga & Maite Mart�nez-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.
  6. 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 225-43, May-June.
  7. 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.
  8. 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 428-35, October.
  9. 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.
  10. 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.
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Cited by:
  1. Markus Jochmann, 2009. "What Belongs Where? Variable Selection for Zero-Inflated Count Models with an Application to the Demand for Health Care," Working Paper Series 45_09, The Rimini Centre for Economic Analysis, revised Jan 2009.
  2. Burda, Martin & Harding, Matthew & Hausman, Jerry, 2012. "A Poisson mixture model of discrete choice," Journal of Econometrics, Elsevier, vol. 166(2), pages 184-203.
  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. 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).
  5. Allison Davis & Klaus Moeltner, 2009. "Valuing the Prevention of an Infestation: The Threat of the New Zealand Mud Snail in Northern Nevada," Working Papers 09-001, University of Nevada, Reno, Department of Economics & University of Nevada, Reno , Department of Resource Economics.
  6. Zheng, Xiaoyong, 2008. "Semiparametric Bayesian estimation of mixed count regression models," Economics Letters, Elsevier, vol. 100(3), pages 435-438, September.
  7. Thomas Brenner & Claudia Werker, 2007. "A Taxonomy of Inference in Simulation Models," Computational Economics, Society for Computational Economics, vol. 30(3), pages 227-244, October.

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