IDEAS home Printed from https://ideas.repec.org/p/yor/hectdg/11-31.html
   My bibliography  Save this paper

Applying Beta-type Size Distributions to Healthcare Cost Regressions

Author

Abstract

This paper extends the literature on modelling healthcare cost data by applying the Generalised Beta of the Second Kind (GB2) distribution to UK data. A quasi-experimental design, estimating models on a subset of the data and evaluating performance on another subset, is used to compare this distribution with its nested and limiting cases. We nd that the GB2 may be a useful tool for choosing an appropriate distribution to apply, with the Beta-2 (B2) distribution and Generalised Gamma (GG) distribution performing the best with this dataset.

Suggested Citation

  • 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.
  • Handle: RePEc:yor:hectdg:11/31
    as

    Download full text from publisher

    File URL: https://www.york.ac.uk/media/economics/documents/herc/wp/11_31.pdf
    File Function: Main text
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. Cummins, J. David & Dionne, Georges & McDonald, James B. & Pritchett, B. Michael, 1990. "Applications of the GB2 family of distributions in modeling insurance loss processes," Insurance: Mathematics and Economics, Elsevier, vol. 9(4), pages 257-272, December.
    2. McDonald, James B, 1984. "Some Generalized Functions for the Size Distribution of Income," Econometrica, Econometric Society, vol. 52(3), pages 647-663, May.
    3. Sun, Jiafeng & Frees, Edward W. & Rosenberg, Marjorie A., 2008. "Heavy-tailed longitudinal data modeling using copulas," Insurance: Mathematics and Economics, Elsevier, vol. 42(2), pages 817-830, April.
    4. Manning, Willard G. & Basu, Anirban & Mullahy, John, 2005. "Generalized modeling approaches to risk adjustment of skewed outcomes data," Journal of Health Economics, Elsevier, vol. 24(3), pages 465-488, May.
    5. McDonald, James B & Butler, Richard J, 1987. "Some Generalized Mixture Distributions with an Application to Unemployment Duration," The Review of Economics and Statistics, MIT Press, vol. 69(2), pages 232-240, May.
    6. Anirban Basu & Willard G. Manning & John Mullahy, 2004. "Comparing alternative models: log vs Cox proportional hazard?," Health Economics, John Wiley & Sons, Ltd., vol. 13(8), pages 749-765.
    7. McDonald, James B. & Xu, Yexiao J., 1995. "A generalization of the beta distribution with applications," Journal of Econometrics, Elsevier, vol. 69(2), pages 427-428, October.
    8. Buntin, Melinda Beeuwkes & Zaslavsky, Alan M., 2004. "Too much ado about two-part models and transformation?: Comparing methods of modeling Medicare expenditures," Journal of Health Economics, Elsevier, vol. 23(3), pages 525-542, May.
    9. 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.
    10. 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.
    11. Anirban Basu & Bhakti V. Arondekar & Paul J. Rathouz, 2006. "Scale of interest versus scale of estimation: comparing alternative estimators for the incremental costs of a comorbidity," Health Economics, John Wiley & Sons, Ltd., vol. 15(10), pages 1091-1107.
    12. Parker, Simon C., 1999. "The generalised beta as a model for the distribution of earnings," Economics Letters, Elsevier, vol. 62(2), pages 197-200, February.
    13. Donna B. Gilleskie & Thomas A. Mroz, 2000. "Estimating the Effects of Covariates on Health Expenditures," NBER Working Papers 7942, National Bureau of Economic Research, Inc.
    14. Arrow, Kenneth J & Lind, Robert C, 1970. "Uncertainty and the Evaluation of Public Investment Decisions," American Economic Review, American Economic Association, vol. 60(3), pages 364-378, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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:bla:jorssc:v:66:y:2017:i:2:p:273-294 is not listed on IDEAS
    3. Davillas, A.; & Jones, A.M.;, 2018. "Parametric models for biomarkers based on flexible size distributions," Health, Econometrics and Data Group (HEDG) Working Papers 18/05, HEDG, c/o Department of Economics, University of York.
    4. Andrew M. Jones & James Lomas & Peter T. Moore & Nigel Rice, 2016. "A quasi-Monte-Carlo comparison of parametric and semiparametric regression methods for heavy-tailed and non-normal data: an application to healthcare costs," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(4), pages 951-974, October.
    5. Peter Zweifel, 2012. "The Grossman model after 40 years," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 13(6), pages 677-682, December.
    6. Davillas, A.; & Jones, A.M.;, 2018. "Parametric models for biomarkers based on flexible size distributions," Health, Econometrics and Data Group (HEDG) Working Papers 18/05, HEDG, c/o Department of Economics, University of York.

    More about this item

    Keywords

    Health econometrics; Generalised beta of the second kind; Generalised gamma; Skewed outcomes; Healthcare cost data;

    JEL classification:

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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:yor:hectdg:11/31. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Jane Rawlings). General contact details of provider: http://edirc.repec.org/data/deyoruk.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.