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Common mortality modeling and coherent forecasts. An empirical analysis of worldwide mortality data

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  • Hatzopoulos, P.
  • Haberman, S.
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    Abstract

    A new common mortality modeling structure is presented for analyzing mortality dynamics for a pool of countries, under the framework of generalized linear models (GLM). The countries are first classified by fuzzy c-means cluster analysis in order to construct the common sparse age-period model structure for the mortality experience. Next, we propose a method to create the common sex difference age-period model structure and then use this to produce the residual age-periodmodel structure for each country and sex. The time related principal components are extrapolated using dynamic linear regression (DLR) models and coherent mortality forecasts are investigated. We make use of mortality data from the “Human Mortality Database”.

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    Bibliographic Info

    Article provided by Elsevier in its journal Insurance: Mathematics and Economics.

    Volume (Year): 52 (2013)
    Issue (Month): 2 ()
    Pages: 320-337

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    Handle: RePEc:eee:insuma:v:52:y:2013:i:2:p:320-337

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    Web page: http://www.elsevier.com/locate/inca/505554

    Related research

    Keywords: Fuzzy c-means cluster; Generalized linear models; Sparse principal component analysis; Dynamic linear regression; Mortality forecasting; Residuals; Coherent;

    References

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    1. Harvey,Andrew C., 1991. "Forecasting, Structural Time Series Models and the Kalman Filter," Cambridge Books, Cambridge University Press, Cambridge University Press, number 9780521405737.
    2. Shiro Horiuchi & John Wilmoth, 1998. "Deceleration in the age pattern of mortality at olderages," Demography, Springer, Springer, vol. 35(4), pages 391-412, November.
    3. Chris Wilson, 2001. "On the Scale of Global Demographic Convergence 1950-2000," Population and Development Review, The Population Council, Inc., The Population Council, Inc., vol. 27(1), pages 155-171.
    4. Hatzopoulos, P. & Haberman, S., 2009. "A parameterized approach to modeling and forecasting mortality," Insurance: Mathematics and Economics, Elsevier, Elsevier, vol. 44(1), pages 103-123, February.
    5. Kevin M. White, 2002. "Longevity Advances in High-Income Countries, 1955-96," Population and Development Review, The Population Council, Inc., The Population Council, Inc., vol. 28(1), pages 59-76.
    6. Renshaw, A. E. & Haberman, S., 2003. "Lee-Carter mortality forecasting with age-specific enhancement," Insurance: Mathematics and Economics, Elsevier, Elsevier, vol. 33(2), pages 255-272, October.
    7. Nan Li & Ronald Lee, 2005. "Coherent mortality forecasts for a group of populations: An extension of the lee-carter method," Demography, Springer, Springer, vol. 42(3), pages 575-594, August.
    8. Hatzopoulos, P. & Haberman, S., 2011. "A dynamic parameterization modeling for the age-period-cohort mortality," Insurance: Mathematics and Economics, Elsevier, Elsevier, vol. 49(2), pages 155-174, September.
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