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European Journal of Operational Research
Article . 2008 . Peer-reviewed
License: Elsevier TDM
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Article . 2008
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Modelling and forecasting mortality in Spain

Authors: Ana Debón; Francisco Montes; Francisco Puig;

Modelling and forecasting mortality in Spain

Abstract

[EN] Experience shows that static life tables overestimate death probabilities. As a consequence of this overestimation the premiums for annuities, pensions and life insurance are not what they actually should be, with negative effects for insurance companies or policy-holders. The reason for this overestimation is that static life tables, through being computed for a specific period of time, cannot take into account the decreasing mortality trend over time. Dynamic life tables overcome this problem by incorporating the influence of the calendar when graduating mortality. Recent papers on the topic look for the development of new methods to deal with this dynamism. Most methods used in dynamic tables are parametric, apply traditional mortality laws and then analyse the evolution of estimated parameters with time series techniques. Our contribution consists in extending and applying Lee–Carter methods to Spanish mortality data, exploring residuals and future trends.

This work was partially supported by a grant from MEyC (Ministerio de Educacio´n y Ciencia, Spain, project MTM-2004-06231). The research of Francisco Montes has also been partially supported by a grant from DGITT (Direccio´ General d’Investigacio´ i Transfere`ncia Tecnolo`gica de la Generalitat Valenciana, project GRUPOS03/189).

Keywords

Applications of statistics to actuarial sciences and financial mathematics, Lee–Carter, dynamic life tables, ESTADISTICA E INVESTIGACION OPERATIVA, forecasting, Lee-Carter method, Mathematical geography and demography, Dynamic life tables, bootstrap confidence intervals, Risk theory, insurance, Forecasting, Bootstrap confidence intervals

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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