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Killing off cohorts: Forecasting mortality of non-extinct cohorts with the penalized composite link model

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  • Rizzi, Silvia
  • Kjærgaard, Søren
  • Bergeron Boucher, Marie-Pier
  • Camarda, Carlo Giovanni
  • Lindahl-Jacobsen, Rune
  • Vaupel, James W.

Abstract

Mortality forecasting has crucial implications for insurance and pension policies. A large amount of literature has proposed models to forecast mortality using cross-sectional (period) data instead of longitudinal (cohort) data. As a consequence, decisions are generally based on period life tables and summary measures such as period life expectancy, which reflect hypothetical mortality rather than the mortality actually experienced by a cohort. This study introduces a novel method to forecast cohort mortality and the cohort life expectancy of non-extinct cohorts. The intent is to complete the mortality profile of cohorts born up to 1960. The proposed method is based on the penalized composite link model for ungrouping data. The performance of the method is investigated using cohort mortality data retrieved from the Human Mortality Database for England & Wales, Sweden, and Switzerland for male and female populations.

Suggested Citation

  • Rizzi, Silvia & Kjærgaard, Søren & Bergeron Boucher, Marie-Pier & Camarda, Carlo Giovanni & Lindahl-Jacobsen, Rune & Vaupel, James W., 2021. "Killing off cohorts: Forecasting mortality of non-extinct cohorts with the penalized composite link model," International Journal of Forecasting, Elsevier, vol. 37(1), pages 95-104.
    • Handle: RePEc:eee:intfor:v:37:y:2021:i:1:p:95-104
    DOI: 10.1016/j.ijforecast.2020.03.003
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    1. Marie-Pier Bergeron-Boucher & Vladimir Canudas-Romo & James E. Oeppen & 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. Søren Kjærgaard & Yunus Emre Ergemen & Malene Kallestrup-Lamb & Jim Oeppen & Rune Lindahl-Jacobsen, 2019. "Forecasting Causes of Death using Compositional Data Analysis: the Case of Cancer Deaths," CREATES Research Papers 2019-07, Department of Economics and Business Economics, Aarhus University.
    3. 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.
    4. 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.
    5. Lenny Stoeldraijer & Coen van Duin & Leo van Wissen & Fanny Janssen, 2013. "Impact of different mortality forecasting methods and explicit assumptions on projected future life expectancy: The case of the Netherlands," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 29(13), pages 323-354.
    6. Andrew Cairns & David Blake & Kevin Dowd & Guy Coughlan & David Epstein & Alen Ong & Igor Balevich, 2009. "A Quantitative Comparison of Stochastic Mortality Models Using Data From England and Wales and the United States," North American Actuarial Journal, Taylor & Francis Journals, vol. 13(1), pages 1-35.
    7. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
    8. 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.
    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. Søren Kjærgaard & Yunus Emre Ergemen & Malene Kallestrup‐Lamb & Jim Oeppen & Rune Lindahl‐Jacobsen, 2019. "Forecasting causes of death by using compositional data analysis: the case of cancer deaths," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 68(5), pages 1351-1370, November.
    11. Samuel Preston & Haidong Wang, 2006. "Sex mortality differences in The United States: The role of cohort smoking patterns," Demography, Springer;Population Association of America (PAA), vol. 43(4), pages 631-646, November.
    12. Booth, H. & Tickle, L., 2008. "Mortality Modelling and Forecasting: a Review of Methods," Annals of Actuarial Science, Cambridge University Press, vol. 3(1-2), pages 3-43, September.
    13. Ugofilippo Basellini & Carlo Giovanni Camarda, 2019. "Modelling and forecasting adult age-at-death distributions," Population Studies, Taylor & Francis Journals, vol. 73(1), pages 119-138, January.
    14. Marie-Pier Bergeron-Boucher & Violetta Simonacci & Jim Oeppen & Michele Gallo, 2018. "Coherent Modeling and Forecasting of Mortality Patterns for Subpopulations Using Multiway Analysis of Compositions: An Application to Canadian Provinces and Territories," North American Actuarial Journal, Taylor & Francis Journals, vol. 22(1), pages 92-118, January.
    15. Carlo Giovanni Camarda, 2019. "Smooth constrained mortality forecasting," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 41(38), pages 1091-1130.
    16. Chiou, Jeng-Min & Müller, Hans-Georg, 2009. "Modeling Hazard Rates as Functional Data for the Analysis of Cohort Lifetables and Mortality Forecasting," Journal of the American Statistical Association, American Statistical Association, vol. 104(486), pages 572-585.
    17. Renshaw, A.E. & Haberman, S., 2006. "A cohort-based extension to the Lee-Carter model for mortality reduction factors," Insurance: Mathematics and Economics, Elsevier, vol. 38(3), pages 556-570, June.
    18. Camarda, Carlo G., 2012. "MortalitySmooth: An R Package for Smoothing Poisson Counts with P-Splines," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 50(i01).
    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.
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    2. Ricarda Duerst & Jonas Schöley & Christina Bohk-Ewald, 2023. "A validation workflow for mortality forecasting," MPIDR Working Papers WP-2023-020, Max Planck Institute for Demographic Research, Rostock, Germany.
    3. Shang, Han Lin & Haberman, Steven & Xu, Ruofan, 2022. "Multi-population modelling and forecasting life-table death counts," Insurance: Mathematics and Economics, Elsevier, vol. 106(C), pages 239-253.
    4. van Raalte, Alyson A & Basellini, Ugofilippo & Camarda, Carlo Giovanni & Nepomuceno, Marília & Myrskylä, Mikko, 2022. "The dangers of drawing cohort profiles from period data: a research note," SocArXiv frkcw, Center for Open Science.
    5. Alyson van Raalte & Ugofilippo Basellini & Carlo Giovanni Camarda & Marília R. Nepomuceno & Mikko Myrskylä, 2022. "The dangers of drawing cohort profiles from period data: a research note," Working Papers ayadh-ohbnm4x3q6cor1, French Institute for Demographic Studies.

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