IDEAS home Printed from https://ideas.repec.org/a/taf/uaajxx/v23y2019i3p335-363.html
   My bibliography  Save this article

Statistical Inference for Lee-Carter Mortality Model and Corresponding Forecasts

Author

Listed:
  • Qing Liu
  • Chen Ling
  • Liang Peng

Abstract

Although the Lee-Carter model has become a benchmark in modeling mortality rates, forecasting mortality risk, and hedging longevity risk, some serious issues exist on its inference and interpretation in the actuarial science literature. After pointing out these pitfalls, this article proposes a modified Lee-Carter model, provides a rigorous statistical inference, and derives the asymptotic distributions of the proposed estimators and unit root test when the mortality index is nearly integrated and errors in the model satisfy some mixing conditions. After a unit root hypothesis is not rejected, future mortality forecasts can be obtained via the proposed inference. An application of the proposed unit root test to U.S. mortality rates rejects the unit root hypothesis for the female and combined mortality rates but does not reject the unit root hypothesis for the male mortality rates.

Suggested Citation

  • Qing Liu & Chen Ling & Liang Peng, 2019. "Statistical Inference for Lee-Carter Mortality Model and Corresponding Forecasts," North American Actuarial Journal, Taylor & Francis Journals, vol. 23(3), pages 335-363, July.
    • Handle: RePEc:taf:uaajxx:v:23:y:2019:i:3:p:335-363
    DOI: 10.1080/10920277.2018.1556702
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/10920277.2018.1556702
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/10920277.2018.1556702?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

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


    Cited by:

    1. Deyuan Li & Chen Ling & Qing Liu & Liang Peng, 2022. "Inference for the Lee-Carter Model With An AR(2) Process," Methodology and Computing in Applied Probability, Springer, vol. 24(2), pages 991-1019, June.
    2. de Jong, Piet & Tickle, Leonie & Xu, Jianhui, 2020. "A more meaningful parameterization of the Lee–Carter model," Insurance: Mathematics and Economics, Elsevier, vol. 94(C), pages 1-8.

    More about this item

    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:taf:uaajxx:v:23:y:2019:i:3:p:335-363. See general information about how to correct material in RePEc.

    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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/uaaj .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.