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A modal age at death approach to forecasting mortality

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  • Bergeron-Boucher, Marie-Pier
  • Vázquez-Castillo, Paola
  • Missov, Trifon

Abstract

Recent studies have shown that there are some advantages in forecasting mortality with other indicators than death rates. In particular, the age-at-death distribution provides readily available information on central longevity measures: mean, median and mode, as well as information on lifespan variation. The modal age at death has been increasing linearly since the second half of the 20th century, providing a strong basis to extrapolate past trends. We develop a model to forecast the age-at-death distribution that directly forecasts the modal age at death and lifespan variation while accounting for dependence between ages. We forecast mortality at age 40 and above in six Western European countries. The introduced model increases forecast accuracy compared with other forecasting models and provides consistent trends in life expectancy and lifespan variation at age 40 over time.

Suggested Citation

  • Bergeron-Boucher, Marie-Pier & Vázquez-Castillo, Paola & Missov, Trifon, 2022. "A modal age at death approach to forecasting mortality," SocArXiv 5zr2k_v1, Center for Open Science.
    • Handle: RePEc:osf:socarx:5zr2k_v1
    DOI: 10.31219/osf.io/5zr2k_v1
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    References listed on IDEAS

    as
    1. Marie-Pier Bergeron-Boucher & James E. Oeppen & Vladimir Canudas-Romo & James W. Vaupel, 2017. "Coherent forecasts of mortality with compositional data analysis," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 37(17), pages 527-566.
    2. Marie-Pier Bergeron-Boucher & James E. Oeppen & James W. Vaupel & Søren Kjærgaard, 2019. "The impact of the choice of life table statistics when forecasting mortality," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 41(43), pages 1235-1268.
    3. Rob Hyndman & Heather Booth & Farah Yasmeen, 2013. "Coherent Mortality Forecasting: The Product-Ratio Method With Functional Time Series Models," Demography, Springer;Population Association of America (PAA), vol. 50(1), pages 261-283, February.
    4. Ronald Lee & Timothy Miller, 2001. "Evaluating the performance of the lee-carter method for forecasting mortality," Demography, Springer;Population Association of America (PAA), vol. 38(4), pages 537-549, November.
    5. Vladimir Canudas-Romo, 2010. "Three measures of longevity: Time trends and record values," Demography, Springer;Population Association of America (PAA), vol. 47(2), pages 299-312, May.
    6. Roland Rau & Eugeny Soroko & Domantas Jasilionis & James W. Vaupel, 2008. "Continued Reductions in Mortality at Advanced Ages," Population and Development Review, The Population Council, Inc., vol. 34(4), pages 747-768, December.
    7. James Vaupel & Vladimir Romo, 2003. "Decomposing change in life expectancy: A bouquet of formulas in honor of Nathan Keyfitz’s 90th birthday," Demography, Springer;Population Association of America (PAA), vol. 40(2), pages 201-216, May.
    8. Ryan D. Edwards & Shripad Tuljapurkar, 2005. "Inequality in Life Spans and a New Perspective on Mortality Convergence Across Industrialized Countries," Population and Development Review, The Population Council, Inc., vol. 31(4), pages 645-674, December.
    9. Andrew J. G. Cairns & David Blake & Kevin Dowd, 2006. "A Two‐Factor Model for Stochastic Mortality with Parameter Uncertainty: Theory and Calibration," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 73(4), pages 687-718, December.
    10. Nan Li & Ronald Lee, 2005. "Coherent mortality forecasts for a group of populations: An extension of the lee-carter method," Demography, Springer;Population Association of America (PAA), vol. 42(3), pages 575-594, August.
    11. Fanny Janssen & Leo Wissen & Anton Kunst, 2013. "Including the Smoking Epidemic in Internationally Coherent Mortality Projections," Demography, Springer;Population Association of America (PAA), vol. 50(4), pages 1341-1362, August.
    12. Shiro Horiuchi & Nadine Ouellette & Siu Lan Karen Cheung & Jean-Marie Robine, 2013. "Modal age at death: lifespan indicator in the era of longevity extension," Vienna Yearbook of Population Research, Vienna Institute of Demography (VID) of the Austrian Academy of Sciences in Vienna, vol. 11(1), pages 37-69.
    13. Marius D. Pascariu & Ugofilippo Basellini & José Manuel Aburto & Vladimir Canudas-Romo, 2020. "The Linear Link: Deriving Age-Specific Death Rates from Life Expectancy," Risks, MDPI, vol. 8(4), pages 1-18, October.
    14. Marie-Pier Bergeron-Boucher & Søren Kjærgaard, 2022. "Mortality forecasting at age 65 and above: an age-specific evaluation of the Lee-Carter model," Scandinavian Actuarial Journal, Taylor & Francis Journals, vol. 2022(1), pages 64-79, January.
    15. repec:cai:popine:popu_p2001_13n1_0171 is not listed on IDEAS
    16. Heather Booth & Rob Hyndman & Piet de Jong & Leonie Tickle, 2006. "Lee-Carter mortality forecasting: a multi-country comparison of variants and extensions," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 15(9), pages 289-310.
    17. Viorela Diaconu & Nadine Ouellette & Robert Bourbeau, 2020. "Modal lifespan and disparity at older ages by leading causes of death: a Canada-U.S. comparison," Journal of Population Research, Springer, vol. 37(4), pages 323-344, December.
    18. Christina Bohk-Ewald & Marcus Ebeling & Roland Rau, 2017. "Lifespan Disparity as an Additional Indicator for Evaluating Mortality Forecasts," Demography, Springer;Population Association of America (PAA), vol. 54(4), pages 1559-1577, August.
    19. Carter, Lawrence R. & Lee, Ronald D., 1992. "Modeling and forecasting US sex differentials in mortality," International Journal of Forecasting, Elsevier, vol. 8(3), pages 393-411, November.
    20. Pascariu, Marius D. & Canudas-Romo, Vladimir & Vaupel, James W., 2018. "The double-gap life expectancy forecasting model," Insurance: Mathematics and Economics, Elsevier, vol. 78(C), pages 339-350.
    21. Marie-Pier Bergeron-Boucher & Vladimir Canudas-Romo & Marcus Ebeling, 2015. "Decomposing changes in life expectancy: Compression versus shifting mortality," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 33(14), pages 391-424.
    22. Nadine Ouellette & Robert Bourbeau, 2011. "Changes in the age-at-death distribution in four low mortality countries: A nonparametric approach," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 25(19), pages 595-628.
    23. Torri, Tiziana & Vaupel, James W., 2012. "Forecasting life expectancy in an international context," International Journal of Forecasting, Elsevier, vol. 28(2), pages 519-531.
    24. Vladimir Canudas-Romo, 2008. "The modal age at death and the shifting mortality hypothesis," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 19(30), pages 1179-1204.
    25. José Manuel Aburto & Alyson van Raalte, 2018. "Lifespan Dispersion in Times of Life Expectancy Fluctuation: The Case of Central and Eastern Europe," Demography, Springer;Population Association of America (PAA), vol. 55(6), pages 2071-2096, December.
    26. Nan Li & Ronald Lee & Patrick Gerland, 2013. "Extending the Lee-Carter Method to Model the Rotation of Age Patterns of Mortality Decline for Long-Term Projections," Demography, Springer;Population Association of America (PAA), vol. 50(6), pages 2037-2051, December.
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