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Progress in Medicine, Limits to Life and Forecasting Mortality

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  • Carlo Favero
  • Marco Giacoletti

Abstract

In this paper we propose a model to forecast future mortality that includes information on the limits to life and on progress in medicine. We apply the model to forecasting future mortality and survival rates for the males population in England andWales. Our proposal extends the benchmark stochastic mortality model along two dimensions. First, we try and deal explicitly with tail risk in the cross-sectional estimation. by including information about the "limit to life" in the sample used to construct factors for the cross-sectional dimension of mortality rates. Second, we propose to substitute the usual stochastic trend model adopted for the time series of risk factors with a predictive framework based on available evidence on medical progress and causes of death. The model projects very little variability for limits to life over the next ten years and predicts that in 2020 the probability that an individual age 65 will survive until 85 is 20% with an upper bound of 23% and a lower bound of 17%.

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  • Carlo Favero & Marco Giacoletti, 2011. "Progress in Medicine, Limits to Life and Forecasting Mortality," Working Papers 406, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    • Handle: RePEc:igi:igierp:406
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    References listed on IDEAS

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    1. Cairns, Andrew J.G. & Blake, David & Dowd, Kevin & Coughlan, Guy D. & Epstein, David & Khalaf-Allah, Marwa, 2011. "Mortality density forecasts: An analysis of six stochastic mortality models," Insurance: Mathematics and Economics, Elsevier, vol. 48(3), pages 355-367, May.
    2. David Blake & Tom Boardman & Andrew Cairns, 2014. "Sharing Longevity Risk: Why Governments Should Issue Longevity Bonds," North American Actuarial Journal, Taylor & Francis Journals, vol. 18(1), pages 258-277.
    3. Andrew Cairns & David Blake & Kevin Dowd & Guy Coughlan & David Epstein & Alen Ong & Igor Balevich, 2009. "A Quantitative Comparison of Stochastic Mortality Models Using Data From England and Wales and the United States," North American Actuarial Journal, Taylor & Francis Journals, vol. 13(1), pages 1-35.
    4. Andrew J. G. Cairns & David Blake & Kevin Dowd, 2006. "A Two‐Factor Model for Stochastic Mortality with Parameter Uncertainty: Theory and Calibration," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 73(4), pages 687-718, December.
    5. Vladimir Canudas-Romo, 2008. "The modal age at death and the shifting mortality hypothesis," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 19(30), pages 1179-1204.
    6. Renshaw, A.E. & Haberman, S., 2006. "A cohort-based extension to the Lee-Carter model for mortality reduction factors," Insurance: Mathematics and Economics, Elsevier, vol. 38(3), pages 556-570, June.
    7. Carter, Lawrence R. & Lee, Ronald D., 1992. "Modeling and forecasting US sex differentials in mortality," International Journal of Forecasting, Elsevier, vol. 8(3), pages 393-411, November.
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    Cited by:

    1. Danan Gu & Patrick Gerland & Kirill F. Andreev & Nan Li & Thomas Spoorenberg & Gerhard Heilig, 2013. "Old age mortality in Eastern and South-Eastern Asia," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 29(38), pages 999-1038.

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