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Modelling and forecasting mortality in Spain


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  • Debón, A.
  • Montes, F.
  • Puig, F.
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    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.

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    Bibliographic Info

    Article provided by Elsevier in its journal European Journal of Operational Research.

    Volume (Year): 189 (2008)
    Issue (Month): 3 (September)
    Pages: 624-637

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    Handle: RePEc:eee:ejores:v:189:y:2008:i:3:p:624-637

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    Cited by:
    1. Ornelas, Arelly & Guillén, Montserrat, 2013. "A Comparison between General Population Mortality and Life Tables for Insurance in Mexico under Gender Proportion Inequality || Una comparación entre la mortalidad de la población general y las tabl," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 16(1), pages 47-67, December.
    2. Debón, A. & Martínez-Ruiz, F. & Montes, F., 2010. "A geostatistical approach for dynamic life tables: The effect of mortality on remaining lifetime and annuities," Insurance: Mathematics and Economics, Elsevier, vol. 47(3), pages 327-336, December.
    3. Katja Hanewald & Thomas Post & Helmut Gr�ndl, 2011. "Stochastic Mortality, Macroeconomic Risks and Life Insurer Solvency," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan, vol. 36(3), pages 458-475, July.
    4. Thomas Post & Katja Hanewald, 2011. "Longevity Risk, Subjective Survival Expectations, and Individual Saving Behavior," Working Papers 201111, ARC Centre of Excellence in Population Ageing Research (CEPAR), Australian School of Business, University of New South Wales.
    5. A. Debòn & S. Haberman & F. Montes & E. Otranto, 2012. "Model effect on projected mortality indicators," Working Paper CRENoS 201215, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    6. Katja Hanewald, 2009. "Mortality modeling: Lee-Carter and the macroeconomy," SFB 649 Discussion Papers SFB649DP2009-008, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.


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