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A robust approach for skewed and heavy-tailed outcomes in the analysis of health care expenditures

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  • Eva Cantoni
  • Elvezio Ronchetti

Abstract

In this paper robust statistical procedures are presented for the analysis of skewed and heavy-tailed outcomes as they typically occur in health care data. The new estimators and test statistics are extensions of classical maximum likelihood techniques for generalized linear models. In contrast to their classical counterparts, the new robust techniques show lower variability and excellent effciency properties in the presence of small deviations form the assumed model, i.e. when the underlying distribution of the data lies in a neighborhood of the model. A simulation study, an analysis on real data, and a sensitivity analysis confirm the good theoretical statistical properties of the new techniques.

Suggested Citation

  • Eva Cantoni & Elvezio Ronchetti, 2004. "A robust approach for skewed and heavy-tailed outcomes in the analysis of health care expenditures," Research Papers by the Institute of Economics and Econometrics, Geneva School of Economics and Management, University of Geneva 2004.03, Institut d'Economie et Econométrie, Université de Genève.
  • Handle: RePEc:gen:geneem:2004.03
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    References listed on IDEAS

    as
    1. Gilleskie, Donna B. & Mroz, Thomas A., 2004. "A flexible approach for estimating the effects of covariates on health expenditures," Journal of Health Economics, Elsevier, vol. 23(2), pages 391-418, March.
    2. Manning, Willard G, et al, 1987. "Health Insurance and the Demand for Medical Care: Evidence from a Randomized Experiment," American Economic Review, American Economic Association, vol. 77(3), pages 251-277, June.
    3. Mullahy, John, 1998. "Much ado about two: reconsidering retransformation and the two-part model in health econometrics," Journal of Health Economics, Elsevier, vol. 17(3), pages 247-281, June.
    4. Mullahy, John, 1997. "Heterogeneity, Excess Zeros, and the Structure of Count Data Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(3), pages 337-350, May-June.
    5. Mullahy, John, 1986. "Specification and testing of some modified count data models," Journal of Econometrics, Elsevier, vol. 33(3), pages 341-365, December.
    6. Andrew M. Jones, 2012. "health econometrics," The New Palgrave Dictionary of Economics, Palgrave Macmillan.
    7. Manning, Willard G., 1998. "The logged dependent variable, heteroscedasticity, and the retransformation problem," Journal of Health Economics, Elsevier, vol. 17(3), pages 283-295, June.
    8. Cantoni E. & Ronchetti E., 2001. "Robust Inference for Generalized Linear Models," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1022-1030, September.
    9. Blough, David K. & Madden, Carolyn W. & Hornbrook, Mark C., 1999. "Modeling risk using generalized linear models," Journal of Health Economics, Elsevier, vol. 18(2), pages 153-171, April.
    10. Manning, Willard G. & Mullahy, John, 2001. "Estimating log models: to transform or not to transform?," Journal of Health Economics, Elsevier, vol. 20(4), pages 461-494, July.
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    More about this item

    Keywords

    Deviations from the model; GLM modeling; health econometrics; heavy tails; robust estimation; robust inference;

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • I10 - Health, Education, and Welfare - - Health - - - General

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