IDEAS home Printed from https://ideas.repec.org/a/eee/insuma/v50y2012i3p309-333.html
   My bibliography  Save this article

Parametric mortality improvement rate modelling and projecting

Author

Listed:
  • Haberman, Steven
  • Renshaw, Arthur

Abstract

We investigate the modelling of mortality improvement rates and the feasibility of projecting mortality improvement rates (as opposed to projecting mortality rates), using parametric predictor structures that are amenable to simple time series forecasting. This leads to our proposing a parallel dual approach to the direct parametric modelling and projecting of mortality rates. Comparisons of simulated life expectancy predictions (by the cohort method) using the England and Wales population mortality experiences for males and females under a variety of controlled data trimming exercises are presented in detail and comparisons are also made between the parallel modelling approaches.

Suggested Citation

  • Haberman, Steven & Renshaw, Arthur, 2012. "Parametric mortality improvement rate modelling and projecting," Insurance: Mathematics and Economics, Elsevier, vol. 50(3), pages 309-333.
  • Handle: RePEc:eee:insuma:v:50:y:2012:i:3:p:309-333
    DOI: 10.1016/j.insmatheco.2011.11.005
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167668711001272
    Download Restriction: Full text for ScienceDirect subscribers only

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

    References listed on IDEAS

    as
    1. Haberman, Steven & Renshaw, Arthur, 2011. "A comparative study of parametric mortality projection models," Insurance: Mathematics and Economics, Elsevier, vol. 48(1), pages 35-55, January.
    2. Baxter, S. D., 2007. "Should Projections of Mortality Improvements be Subject to a Minimum Value?," British Actuarial Journal, Cambridge University Press, vol. 13(03), pages 375-464, September.
    3. Haberman, Steven & Renshaw, Arthur, 2009. "On age-period-cohort parametric mortality rate projections," Insurance: Mathematics and Economics, Elsevier, vol. 45(2), pages 255-270, October.
    4. Willets, R. C., 2004. "The Cohort Effect: Insights and Explanations," British Actuarial Journal, Cambridge University Press, vol. 10(04), pages 833-877, October.
    5. Plat, Richard, 2009. "On stochastic mortality modeling," Insurance: Mathematics and Economics, Elsevier, vol. 45(3), pages 393-404, December.
    6. Dowd, Kevin & Cairns, Andrew J.G. & Blake, David & Coughlan, Guy D. & Epstein, David & Khalaf-Allah, Marwa, 2010. "Evaluating the goodness of fit of stochastic mortality models," Insurance: Mathematics and Economics, Elsevier, vol. 47(3), pages 255-265, December.
    7. Renshaw, A. E. & Haberman, S., 2003. "Lee-Carter mortality forecasting with age-specific enhancement," Insurance: Mathematics and Economics, Elsevier, vol. 33(2), pages 255-272, October.
    8. Brouhns, Natacha & Denuit, Michel & Vermunt, Jeroen K., 2002. "A Poisson log-bilinear regression approach to the construction of projected lifetables," Insurance: Mathematics and Economics, Elsevier, vol. 31(3), pages 373-393, December.
    9. Pitacco, Ermanno & Denuit, Michel & Haberman, Steven & Olivieri, Annamaria, 2009. "Modelling Longevity Dynamics for Pensions and Annuity Business," OUP Catalogue, Oxford University Press, number 9780199547272.
    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. Haberman, Steven & Renshaw, Arthur, 2013. "Modelling and projecting mortality improvement rates using a cohort perspective," Insurance: Mathematics and Economics, Elsevier, vol. 53(1), pages 150-168.
    2. Mitchell, Daniel & Brockett, Patrick & Mendoza-Arriaga, Rafael & Muthuraman, Kumar, 2013. "Modeling and forecasting mortality rates," Insurance: Mathematics and Economics, Elsevier, vol. 52(2), pages 275-285.
    3. Hunt, Andrew & Blake, David, 2015. "Modelling longevity bonds: Analysing the Swiss Re Kortis bond," Insurance: Mathematics and Economics, Elsevier, vol. 63(C), pages 12-29.
    4. Mammen, Enno & Martínez Miranda, María Dolores & Nielsen, Jens Perch, 2015. "In-sample forecasting applied to reserving and mesothelioma mortality," Insurance: Mathematics and Economics, Elsevier, vol. 61(C), pages 76-86.
    5. Boonen, Tim J. & De Waegenaere, Anja & Norde, Henk, 2017. "Redistribution of longevity risk: The effect of heterogeneous mortality beliefs," Insurance: Mathematics and Economics, Elsevier, vol. 72(C), pages 175-188.
    6. repec:taf:applec:v:49:y:2017:i:2:p:170-187 is not listed on IDEAS
    7. Liu, Yanxin & Li, Johnny Siu-Hang, 2015. "The age pattern of transitory mortality jumps and its impact on the pricing of catastrophic mortality bonds," Insurance: Mathematics and Economics, Elsevier, vol. 64(C), pages 135-150.
    8. Danesi, Ivan Luciano & Haberman, Steven & Millossovich, Pietro, 2015. "Forecasting mortality in subpopulations using Lee–Carter type models: A comparison," Insurance: Mathematics and Economics, Elsevier, vol. 62(C), pages 151-161.
    9. Colin O’hare & Youwei Li, 2017. "Modelling mortality: are we heading in the right direction?," Applied Economics, Taylor & Francis Journals, vol. 49(2), pages 170-187, January.
    10. Helena Chuliá & Montserrat Guillén & Jorge M. Uribe, 2015. "Mortality and Longevity Risks in the United Kingdom: Dynamic Factor Models and Copula-Functions," Working Papers 2015-03, Universitat de Barcelona, UB Riskcenter.
    11. Denuit, Michel & Trufin, Julien, 2016. "From regulatory life tables to stochastic mortality projections: The exponential decline model," Insurance: Mathematics and Economics, Elsevier, vol. 71(C), pages 295-303.
    12. repec:spr:demogr:v:54:y:2017:i:4:d:10.1007_s13524-017-0584-0 is not listed on IDEAS
    13. Christina Bohk & Roland Rau & Joel E. Cohen, 2015. "Taylor's power law in human mortality," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 33(21), pages 589-610, September.

    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:eee:insuma:v:50:y:2012:i:3:p:309-333. 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: (Dana Niculescu). General contact details of provider: http://www.elsevier.com/locate/inca/505554 .

    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.