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A Quasi-experimental Comparison of Econometric Models for Health Care Expenditures

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Abstract

Individual health care expenditures have complex non-normal distributions with severe positive skewness and leptokurtosis. These features present severe challenges to reliable modeling of expenditures for prediction purposes. We compare a variety of methods using quasi-experimental techniques. Our quasi-experiments combine the distributional realism of actual data on health care expenditures with the reliability of Monte Carlo experimental results. We find that models based on Gamma densities predict substantially better than models based on linear regression with and without transformation of the dependent variable. Models based on finite mixtures of Gamma densities show further improvement in predictive properties.

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  • Partha Deb & James F. Burgess, Jr., 2003. "A Quasi-experimental Comparison of Econometric Models for Health Care Expenditures," Economics Working Paper Archive at Hunter College 212, Hunter College Department of Economics.
  • Handle: RePEc:htr:hcecon:212
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    File URL: http://econ.hunter.cuny.edu/wp-content/uploads/sites/6/RePEc/papers/HunterEconWP212.pdf
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    1. Maasoumi, Esfandiar & Phillips, Peter C. B., 1982. "On the behavior of inconsistent instrumental variable estimators," Journal of Econometrics, Elsevier, vol. 19(2-3), pages 183-201, August.
    2. 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.
    3. Andrew M. Jones, 2012. "health econometrics," The New Palgrave Dictionary of Economics, Palgrave Macmillan.
    4. James J. Heckman, 2001. "Micro Data, Heterogeneity, and the Evaluation of Public Policy: Nobel Lecture," Journal of Political Economy, University of Chicago Press, vol. 109(4), pages 673-748, August.
    5. McDonald, James B & Mantrala, Anand, 1995. "The Distribution of Personal Income: Revisited," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(2), pages 201-204, April-Jun.
    6. Hendry, David F., 1982. "A reply to Professors Maasoumi and Phillips," Journal of Econometrics, Elsevier, vol. 19(2-3), pages 203-213, August.
    7. 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.
    8. 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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    Cited by:

    1. Andrew Briggs & Richard Nixon & Simon Dixon & Simon Thompson, 2005. "Parametric modelling of cost data: some simulation evidence," Health Economics, John Wiley & Sons, Ltd., vol. 14(4), pages 421-428.
    2. repec:ris:apltrx:0315 is not listed on IDEAS
    3. Amal Malehi & Fatemeh Pourmotahari & Kambiz Angali, 2015. "Statistical models for the analysis of skewed healthcare cost data: a simulation study," Health Economics Review, Springer, vol. 5(1), pages 1-16, December.
    4. Richard M. Nixon & Simon G. Thompson, 2005. "Methods for incorporating covariate adjustment, subgroup analysis and between-centre differences into cost-effectiveness evaluations," Health Economics, John Wiley & Sons, Ltd., vol. 14(12), pages 1217-1229.
    5. Steven C. Hill & G. Edward Miller, 2010. "Health expenditure estimation and functional form: applications of the generalized gamma and extended estimating equations models," Health Economics, John Wiley & Sons, Ltd., vol. 19(5), pages 608-627.

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