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A quasi-Monte Carlo comparison of developments in parametric and semi-parametric regression methods for heavy tailed and non-normal data: with an application to healthcare costs

Author

Listed:
  • Jones, A. M.
  • Lomas, J.
  • Moore, P.
  • Rice, N.

Abstract

We conduct a quasi-Monte Carlo comparison of the recent developments in parametric and semi-parametric regression methods for healthcare costs against each other and against standard practice. The population of English NHS hospital inpatient episodes for the nancial year 2007-2008 (summed for each patient: 6,164,114 observations in total) is randomly divided into two equally sized sub-populations to form an estimation and a validation set. Evaluating out-of-sample using the validaton set, a conditional density estimator shows considerable promise in forecasting conditional means, performing best for accuracy of forecasting and amongst the best four (of sixteen compared) for bias and goodness-of- t. The best performing model for bias is linear regression with square root transformed dependent variable, and a generalised linear model with square root link function and Poisson distribution performs best in terms of goodness-of- t. Commonly used models utilising a log-link are shown to perform badly relative to other models considered in our comparison.

Suggested Citation

  • Jones, A. M. & Lomas, J. & Moore, P. & Rice, N., 2013. "A quasi-Monte Carlo comparison of developments in parametric and semi-parametric regression methods for heavy tailed and non-normal data: with an application to healthcare costs," Health, Econometrics and Data Group (HEDG) Working Papers 13/30, HEDG, c/o Department of Economics, University of York.
  • Handle: RePEc:yor:hectdg:13/30
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    Citations

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    Cited by:

    1. Jones, A. & Lomas, J. & Rice, N., 2014. "Going Beyond the Mean in Healthcare Cost Regressions: a Comparison of Methods for Estimating the Full Conditional Distribution," Health, Econometrics and Data Group (HEDG) Working Papers 14/26, HEDG, c/o Department of Economics, University of York.
    2. repec:spr:pharme:v:35:y:2017:i:7:d:10.1007_s40273-017-0505-5 is not listed on IDEAS
    3. Paolo Berta & Salvatore Ingrassia & Antonio Punzo & Giorgio Vittadini, 2016. "Multilevel cluster-weighted models for the evaluation of hospitals," METRON, Springer;Sapienza Università di Roma, vol. 74(3), pages 275-292, December.
    4. repec:ris:apltrx:0315 is not listed on IDEAS
    5. Galina Besstremyannaya, 2014. "Heterogeneous effect of coinsurance rate on healthcare costs: generalized finite mixtures and matching estimators," Discussion Papers 14-014, Stanford Institute for Economic Policy Research.

    More about this item

    Keywords

    Health econometrics; healthcare costs; heavy tails; quasi-Monte Carlo;

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling

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