IDEAS home Printed from
   My bibliography  Save this paper

The health-economic applications of copulas: methods in applied econometric research


  • Casey Quinn


A copula is best described, as in Joe (1997), as a multivariate distribution function that is used to bind each marginal distribution function to form the joint. The copula parameterises the dependence between the margins, while the parameters of each marginal distribution function can be estimated separately. This is a brief introduction to copulas and multivariate dependence issues within a health economics context. The research presented here will make its own contributions to the development of copulas as a methodology, but more importantly will make deliberate inroads into health economic applications of copulas. To do this, common analytic problems faced by health economists are considered. Some of the differences between the copula methodology and existing alternatives are discussed, and a generalisable, systematic approach to estimation is provided.

Suggested Citation

  • Casey Quinn, 2007. "The health-economic applications of copulas: methods in applied econometric research," Health, Econometrics and Data Group (HEDG) Working Papers 07/22, HEDG, c/o Department of Economics, University of York.
  • Handle: RePEc:yor:hectdg:07/22

    Download full text from publisher

    File URL:
    File Function: Main text
    Download Restriction: no


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

    Cited by:

    1. Noemi Kreif & Richard Grieve & Rosalba Radice & Zia Sadique & Roland Ramsahai & Jasjeet S. Sekhon, 2012. "Methods for Estimating Subgroup Effects in Cost-Effectiveness Analyses That Use Observational Data," Medical Decision Making, , vol. 32(6), pages 750-763, November.
    2. Bhat, Chandra R. & Eluru, Naveen, 2009. "A copula-based approach to accommodate residential self-selection effects in travel behavior modeling," Transportation Research Part B: Methodological, Elsevier, vol. 43(7), pages 749-765, August.
    3. Ji, Xiangfeng & Chu, Yanyu, 2020. "A target-oriented bi-attribute user equilibrium model with travelers’ perception errors on the tolled traffic network," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 144(C).
    4. José Murteira & Óscar Lourenço, 2011. "Health care utilization and self-assessed health: specification of bivariate models using copulas," Empirical Economics, Springer, vol. 41(2), pages 447-472, October.
    5. Ipek N Sener & Chandra R Bhat, 2011. "A Copula-Based Sample Selection Model of Telecommuting Choice and Frequency," Environment and Planning A, , vol. 43(1), pages 126-145, January.
    6. Chandra Bhat & Ipek Sener, 2009. "A copula-based closed-form binary logit choice model for accommodating spatial correlation across observational units," Journal of Geographical Systems, Springer, vol. 11(3), pages 243-272, September.
    7. Dardati, Evangelina & Saygili, Meryem, 2020. "Aggregate impacts of cap-and-trade programs with heterogeneous firms," Energy Economics, Elsevier, vol. 92(C).
    8. Sener, Ipek N. & Reeder, Phillip R., 2014. "An integrated analysis of workers’ physically active activity and active travel choice behavior," Transportation Research Part A: Policy and Practice, Elsevier, vol. 67(C), pages 381-393.

    More about this item



    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • I3 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty
    • I10 - Health, Education, and Welfare - - Health - - - General

    NEP fields

    This paper has been announced in the following NEP Reports:


    Access and download statistics


    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:07/22. 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: . General contact details of provider: .

    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.

    We have no bibliographic references for this item. You can help adding them by using 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Jane Rawlings (email available below). General contact details of provider: .

    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.