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Improving Overall Mortality Forecasts by Analysing Cause-of-Death, Period and Cohort Effects in Trends

Author

Listed:
  • Ewa Tabeau

    (Netherlands Interdisciplinary Demographic Institute (NIDI))

  • Peter Ekamper

    (Netherlands Interdisciplinary Demographic Institute (NIDI))

  • Corina Huisman

    (Netherlands Interdisciplinary Demographic Institute (NIDI))

  • Alinda Bosch

    (Netherlands Interdisciplinary Demographic Institute (NIDI))

Abstract

The major goal of this study is to propose improvements in the methods for forecasting overall mortality. In order to reach this goal, three types of trend-oriented forecasts have been studied. Each type of forecast is conditional on developments in one of the three factors, period, cohort and cause of death, which are known to represent symptomatic measures of certain causal mechanisms. Mortality projections have been made for four developed European countries: France, Italy, the Netherlands and Norway. The projections are based on observed mortality data over the years 1950--1994 and cohorts born in the nineteenth and twentieth century. The results of the analyses do not show a best solution, though the cause-of-death approach looks the most promising. However, the period and cohort approaches certainly have additional value in the forecasting process. The cause-of-death approach should ideally be used jointly with the overall mortality period (or overall mortality cohort) approach. However, the cause-of-death approach is not optimal for forecasting the mortality of the oldest-old. Another modelling method, for instance parameterization of overall mortality, should be considered for that purpose. The cohort approach can be used to improve forecasting of period mortality.

Suggested Citation

  • Ewa Tabeau & Peter Ekamper & Corina Huisman & Alinda Bosch, 1999. "Improving Overall Mortality Forecasts by Analysing Cause-of-Death, Period and Cohort Effects in Trends," European Journal of Population, Springer;European Association for Population Studies, vol. 15(2), pages 153-183, June.
    • Handle: RePEc:spr:eurpop:v:15:y:1999:i:2:d:10.1023_a:1006109310764
    DOI: 10.1023/A:1006109310764
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    References listed on IDEAS

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    1. McNown, Robert & Rogers, Andrei, 1992. "Forecasting cause-specific mortality using time series methods," International Journal of Forecasting, Elsevier, vol. 8(3), pages 413-432, November.
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    Cited by:

    1. Nhan Huynh & Mike Ludkovski, 2021. "Joint Models for Cause-of-Death Mortality in Multiple Populations," Papers 2111.06631, arXiv.org.
    2. Bergeron-Boucher, Marie-Pier & Kjærgaard, Søren, 2022. "Mortality forecasts by age and cause of death: How to forecast both dimensions?," SocArXiv d7hbp, Center for Open Science.
    3. Li, Han & Li, Hong & Lu, Yang & Panagiotelis, Anastasios, 2019. "A forecast reconciliation approach to cause-of-death mortality modeling," Insurance: Mathematics and Economics, Elsevier, vol. 86(C), pages 122-133.
    4. Camille Delbrouck & Jennifer Alonso-García, 2024. "COVID-19 and Excess Mortality: An Actuarial Study," Risks, MDPI, vol. 12(4), pages 1-27, March.
    5. Nicholas Bett & Juma Kasozi & Daniel Ruturwa, 2022. "Temporal Clustering of the Causes of Death for Mortality Modelling," Risks, MDPI, vol. 10(5), pages 1-34, May.

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