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Estimating and predicting the distribution of the number of visits to the medical doctor

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Author Info

  • Jing Dai

    ()
    (Universität Kassel)

  • Stefan Sperlich

    ()
    (Université de Genève)

  • Walter Zucchini

    ()
    (Georg-August Universität Göttingen)

Abstract

In many countries the demand for health care services is of increasing importance. Especially in the industrialized world with a changing demographic structure social insurances and politics face real challenges. Reliable predictors of those demand functions will therefore become invaluable tools. This article proposes a prediction method for the distribution of the number of visits to the medical doctor for a determined population, given a sample that is not necessarily taken from that population. It uses the estimated conditional sample distribution, and it can be applied for forecast scenarios. The methods are illustrated along data from Sidney. The introduced methodology can be applied as well to any other prediction problem of discrete distributions in real, future or any fictitious population. It is therefore also an excellent tool for future predictions, scenarios and policy evaluation.

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File URL: http://www.uni-marburg.de/fb02/makro/forschung/magkspapers/48-2011_dai.pdf
File Function: First version, 2011
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Bibliographic Info

Paper provided by Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung) in its series MAGKS Papers on Economics with number 201148.

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Length: 17 pages
Date of creation: 2011
Date of revision:
Publication status: Forthcoming in
Handle: RePEc:mar:magkse:201148

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Web page: http://www.uni-marburg.de/fb02/
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Related research

Keywords: predicting health care demand; visits to the doctor; health economics; model selection;

References

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  1. 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.
  2. Stefan Sperlich & Oliver Linton & Wolfgang Härdle, 1999. "Integration and backfitting methods in additive models-finite sample properties and comparison," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer, vol. 8(2), pages 419-458, December.
  3. Stefan Sperlich, 2009. "A note on non-parametric estimation with predicted variables," Econometrics Journal, Royal Economic Society, vol. 12(2), pages 382-395, 07.
  4. Windmeijer, F A G & Silva, J M C Santos, 1997. "Endogeneity in Count Data Models: An Application to Demand for Health Care," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(3), pages 281-94, May-June.
  5. Cameron, A C & P. K. Trivedi & Frank Milne & J. Piggott, 1988. "A Microeconometric Model of the Demand for Health Care and Health Insurance in Australia," Review of Economic Studies, Wiley Blackwell, vol. 55(1), pages 85-106, January.
  6. 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.
  7. R. A. Rigby & D. M. Stasinopoulos, 2005. "Generalized additive models for location, scale and shape," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(3), pages 507-554.
  8. 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.
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