The double-gap life expectancy forecasting model. (English) Zbl 1400.91252

Summary: Life expectancy is highly correlated over time among countries and between males and females. These associations can be used to improve forecasts. Here we propose a method for forecasting female life expectancy based on analysis of the gap between female life expectancy in a country compared with the record level of female life expectancy in the world. Second, to forecast male life expectancy, the gap between male life expectancy and female life expectancy in a country is analysed. We present these results for various developed countries. We compare our results with forecasts based on the Lee-Carter approach and the Cairns-Blake-Dowd strategy. We focus on forecasting life expectancy at age 0 and remaining life expectancy at age 65.


91B30 Risk theory, insurance (MSC2010)
62P05 Applications of statistics to actuarial sciences and financial mathematics
62M20 Inference from stochastic processes and prediction
Full Text: DOI


[1] Austad, S. N., Why women live longer than men: sex differences in longevity, Gender Med., 3, 2, 79-92, (2006)
[2] Booth, H., Hyndman, R., Tickle, L., De Jong, P., 2006. Lee-Carter mortality forecasting: A multi-country comparison of variants and extensions.
[3] Bowerman, B.; O’Connell, R.; Koehler, A., Forecasting: Methods and Applications, (2004), Thomson Brooks/Cole Belmont, CA
[4] Box, G. E.; Jenkins, G. M., Time Series Analysis, Control, and Forecasting, Vol. 3226, 10, (1976), Holden Day San Francisco, CA, 3228
[5] Brass, W., On the scale of mortality, Biol. Asp. Demogr., 69-110, (1971)
[6] Cairns, A. J.; Blake, D.; Dowd, K., A two-factor model for stochastic mortality with parameter uncertainty: theory and calibration, J. Risk Insurance, 73, 4, 687-718, (2006)
[7] Haberman, S., Kaishev, V., Millosaovich, P., Villegas, A., Baxter, S., Gaches, A., Gunnlaugsson, S., Sison, M., 2014. Longevity basis risk: A methodology for assessing basis risk. Institute and Faculty of Actuaries Sessional Research Paper.
[8] Hanke, J. E.; Reitsch, A. G.; Wichern, D. W., Business Forecasting, (2001), Prentice Hall NJ
[9] Human Mortality Database, 2017. University of California, Berkeley (USA), and Max Planck Institutefor Demographic Research (Germany). Available at www.mortality.orgorwww.humanmortality.de (data downloaded on 06/06/17).
[10] Hyndman, R. J.; Booth, H.; Yasmeen, F., Coherent mortality forecasting: the product-ratio method with functional time series models, Demography, 50, 1, 261-283, (2013)
[11] Jarner, S. F.; Kryger, E. M., Modelling adult mortality in small populations: the SAINT model, Astin Bull., 41, 02, 377-418, (2011) · Zbl 1239.91128
[12] Kwiatkowski, D.; Phillips, P. C.; Schmidt, P.; Shin, Y., Testing the null hypothesis of stationarity against the alternative of a unit root: how sure are we that economic time series have a unit root?, J. Econometrics, 54, 1-3, 159-178, (1992) · Zbl 0871.62100
[13] Lee, R. D.; Carter, L. R., Modeling and forecasting U.S. mortality, J. Amer. Statist. Assoc., 87, 419, 659-671, (1992) · Zbl 1351.62186
[14] Li, N.; Lee, R., Coherent mortality forecasts for a group of populations: an extension of the Lee-Carter method, Demography, 42, 3, 575-594, (2005)
[15] Mead, L. R.; Papanicolaou, N., Maximum entropy in the problem of moments, J. Math. Phys., 25, 8, 2404-2417, (1984)
[16] Messner, J., Zeileis, A., 2015. Censored Regression with Conditional Heteroscedasticity, Version: 0.9-1. CRAN - Electronic Resource.
[17] Oeppen, J., Life expectancy convergence among nations Since 1820: separating the effects of technology and income, (Perspectives on Mortality Forecasting. III. the Linear Rise in Life Expectancy: History and Prospects, Social Insurance Studies, (2006)), 55-82, (3)
[18] Oeppen, J.; Vaupel, J. W., Broken limits to life expectancy, Science, 296, 5570, 1029-1031, (2002)
[19] Perls, T. T.; Fretts, R. C., Why women live longer than men - what gives women the extra years?, Sci. Am., 2, 100-103, (1998)
[20] Raftery, A. E.; Chunn, J. L.; Gerland, P.; Ševčíková, H., Bayesian probabilistic projections of life expectancy for all countries, Demography, 50, 3, 777-801, (2013)
[21] Raftery, A. E.; Lalic, N.; Gerland, P., Joint probabilistic projection of female and male life expectancy, Demogr. Res., 30, 795, (2014)
[22] Thatcher, A. R.; Kannisto, V.; Vaupel, J. W., The Force of Mortality at Ages 80 to 120, (1998), Odense University Press Odense, Denmark
[23] Torri, T.; Vaupel, J. W., Forecasting life expectancy in an international context, Int. J. Forecast., 28, 2, 519-531, (2012)
[24] United Nations, 2009. World Population Prospects: The 2008 Revision. New York, NY: United Nations.
[25] Vallin, J.; Meslé, F., The segmented trend line of highest life expectancies, Popul. Dev. Rev., 35, 1, 159-187, (2009)
[26] Villegas, A., Millossovich, P., Kaishev, V., 2015. ‘StMoMo’: An R Package for stochastic mortality modelling.
[27] Whelpton, P. K.; Eldridge, H. T.; Seigel, J. S., Forecasts of the Population of the United States, (1948), U.S. Government Publishing Office
[28] White, K. M., Longevity advances in high-income countries, 1955-96, Popul. Dev. Rev., 28, 1, 59-76, (2002)
[29] Wilmoth, J.; Zureick, S.; Canudas-Romo, V.; Inoue, M.; Sawyer, C., A flexible two-dimensional mortality model for use in indirect estimation, Popul. Stud., 66, 1, 1-28, (2012)
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