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Estimating the Effects of Covariates on Health Expenditures

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  • Donna B. Gilleskie
  • Thomas A. Mroz

Abstract

This paper addresses estimation of an outcome characterized by mass at zero, significant skewness, and heteroscedasticity. Unlike other approaches suggested recently that require retransformations or arbitrary assumptions about error distributions, our estimation strategy uses sequences of conditional probability functions, similar to those used in discrete time hazard rate analyses, to construct a discrete approximation to the density function of the outcome of interest conditional on exogenous explanatory variables. Once the conditional density function has been constructed, we can examine expectations of arbitrary functions of the outcome of interest and evaluate how these expectations vary with observed exogenous covariates. This removes a researcher's reliance on strong and often untested maintained assumptions. We demonstrate the features and precision of the conditional density estimation method through Monte Carlo experiments and an application to health expenditures using the RAND Health Insurance Experiment data. Overall, we find that the approximate conditional density estimator that we propose provides accurate and precise estimates of derivatives of expected outcomes for a wide range of types of explanatory variables. We find that two-part smearing models often used by health economists do not perform well. Our results, both in Monte Carlo experiments and in our real application, also indicate that simple one-part OLS models of level health expenditures can provide more accurate estimates than commonly used two-part models with smearing, provided one uses enough expansion terms in the one-part model to fit the data well.

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Bibliographic Info

Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 7942.

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Date of creation: Oct 2000
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Publication status: published as Gilleskie, Donna and Thomas A. Mroz. “A Flexible Approach for Estimating the Effects of Covariates on Health Expenditures.” Journal of Health Economics 23, 2 (2004): 391-418.
Handle: RePEc:nbr:nberwo:7942

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  1. Willard G. Manning & John Mullahy, 1999. "Estimating Log Models: To Transform or Not to Transform?," NBER Technical Working Papers 0246, National Bureau of Economic Research, Inc.
  2. Joshua D. Angrist, 2000. "Estimation of Limited-Dependent Variable Models with Dummy Endogenous Regressors: Simple Strategies for Empirical Practice," NBER Technical Working Papers 0248, National Bureau of Economic Research, Inc.
  3. John Mullahy, 1998. "Much Ado About Two: Reconsidering Retransformation and the Two-Part Model in Health Economics," NBER Technical Working Papers 0228, National Bureau of Economic Research, Inc.
  4. Heckman, James J & Honore, Bo E, 1990. "The Empirical Content of the Roy Model," Econometrica, Econometric Society, vol. 58(5), pages 1121-49, September.
  5. Heckman, James & Singer, Burton, 1984. "A Method for Minimizing the Impact of Distributional Assumptions in Econometric Models for Duration Data," Econometrica, Econometric Society, vol. 52(2), pages 271-320, March.
  6. Donald, Stephen G & Green, David A & Paarsch, Harry J, 2000. "Differences in Wage Distributions between Canada and the United States: An Application of a Flexible Estimator of Distribution Functions in the Presence of Covariates," Review of Economic Studies, Wiley Blackwell, vol. 67(4), pages 609-33, October.
  7. Mroz, T.A. & Weir, D.R., 1988. "Structural Change In Life Cycle Fertility During The Fertility Transition: France Before And After The Revolution," University of Chicago - Economics Research Center 88-13, Chicago - Economics Research Center.
  8. 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 251-77, June.
  9. Eastwood, Brian J. & Gallant, A. Ronald, 1991. "Adaptive Rules for Seminonparametric Estimators That Achieve Asymptotic Normality," Econometric Theory, Cambridge University Press, vol. 7(03), pages 307-340, September.
  10. repec:cup:etheor:v:7:y:1991:i:3:p:307-40 is not listed on IDEAS
  11. Hahn, Jinyong & Todd, Petra & Van der Klaauw, Wilbert, 2001. "Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design," Econometrica, Econometric Society, vol. 69(1), pages 201-09, January.
  12. Bruce D. Meyer, 1988. "Unemployment Insurance And Unemployment Spells," NBER Working Papers 2546, National Bureau of Economic Research, Inc.
  13. Mroz, Thomas A., 1999. "Discrete factor approximations in simultaneous equation models: Estimating the impact of a dummy endogenous variable on a continuous outcome," Journal of Econometrics, Elsevier, vol. 92(2), pages 233-274, October.
  14. 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.
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Cited by:
  1. Mark E Schweitzer, 2003. "Ready, willing, and able? Measuring labour availability in the UK," Bank of England working papers 186, Bank of England.
  2. Jones, A. & Lomas, J. & Rice, N., 2011. "Applying Beta-type Size Distributions to Healthcare Cost Regressions," Health, Econometrics and Data Group (HEDG) Working Papers 11/31, HEDG, c/o Department of Economics, University of York.
  3. Silviya Nikolova; & Arthur Sinko; & Matt Sutton;, 2012. "Do maximum waiting times guarantees change clinical priorities? A Conditional Density Estimation approach," Health, Econometrics and Data Group (HEDG) Working Papers 12/07, HEDG, c/o Department of Economics, University of York.

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