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Modelling and Forecasting the Mortality of the Very Old

Published online by Cambridge University Press:  09 August 2013

Iain D. Currie
Iain D. Currie*
Affiliation:
Department of Actuarial Mathematics and Statistics and the Maxwell Institute for Mathematical Sciences, Heriot-Watt University, Edinburgh, EH14 4AS, UK. Tel.: +44 (0)131 451 3208, Fax: +44 (0)131 451 3249, E-Mail: I.D.Currie@hw.ac.uk
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Abstract

The forecasting of the future mortality of the very old presents additional challenges since data quality can be poor at such ages. We consider a two-factor model for stochastic mortality, proposed by Cairns, Blake and Dowd, which is particularly well suited to forecasting at very high ages. We consider an extension to their model which improves fit and also allows forecasting at these high ages. We illustrate our methods with data from the Continuous Mortality Investigation.

Keywords

Forecastingvery high agesmortalitysmoothing
Type
Research Article
Information
ASTIN Bulletin: The Journal of the IAA , Volume 41 , Issue 2 , November 2011 , pp. 419 - 427
Copyright
Copyright © International Actuarial Association 2011

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References

Brouhns, N., Denuit, M. and Vermunt, J.K. (2002) A Poisson log- bilinear regression approach to the construction of projected lifetables. Insurance: Mathematics and Economics, 31, 373–393.Google Scholar
Cairns, A.J.G., Blake, D. and Dowd, K. (2006) A two-factor model for stochastic mortality with parameter uncertainty: theory and calibration. Journal of Risk and Insurance, 73, 687–718.Google Scholar
Cairns, A.J.G., Blake, D., Dowd, K., Coughlan, G.D., Epstein, D., Ong, A. and Balevich, I. (2009) A quantitative comparison of stochastic mortality models using data from England and Wales and the United States. North American Actuarial Journal, 13, 1–35.Google Scholar
Currie, I.D., Durban, M. and Eilers, P.H.C. (2004) Smoothing and forecasting mortality rates. Statistical Modelling, 4, 279–298.Google Scholar
Eilers, P.H.C. and Marx, B.D. (1996) Flexible smoothing with B-splines and penalties. Statistical Science, 11, 89–121.Google Scholar
Hastie, T.J. and Tibshirani, R.J. (1990) Generalized Additive Models. London: Chapman and Hall.Google Scholar
Lee, R.D. and Carter, L.R. (1992) Modeling and forecasting U.S. mortality. Journal of the American Statistical Association, 87, 659–675.Google Scholar
McCullagh, P. and Nelder, J.A. (1989) Generalized Linear Models. London: Chapman and Hall.Google Scholar
R Development Core Team (2009) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.Google Scholar
Richards, S.J. and Currie, I.D. (2009) Longevity risk and annuity pricing with the Lee-Carter model. British Actuarial Journal, 15, Part II (to be published).Google Scholar
Richards, S.J., Kirkby, J.G. and Currie, I.D. (2006) The importance of year of birth in two-dimensional mortality data. British Actuarial Journal, 12, 5–61.Google Scholar
Searle, S.R. (1982) Matrix Algebra Useful for Statistics. New York: Wiley.Google Scholar