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Modelling and forecasting adult age-at-death distributions

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  • Ugofilippo Basellini
  • Carlo Giovanni Camarda

Abstract

Age-at-death distributions provide an informative description of the mortality pattern of a population but have generally been neglected for modelling and forecasting mortality. In this paper, we use the distribution of deaths to model and forecast adult mortality. Specifically, we introduce a relational model that relates a fixed ‘standard’ to a series of observed distributions by a transformation of the age axis. The proposed Segmented Transformation Age-at-death Distributions (STAD) model is parsimonious and efficient: using only three parameters, it captures and disentangles mortality developments in terms of shifting and compression dynamics. Additionally, mortality forecasts can be derived from parameter extrapolation using time-series models. We illustrate our method and compare it with the Lee–Carter model and variants for females in four high-longevity countries. We show that the STAD fits the observed mortality pattern very well, and that its forecasts are more accurate and optimistic than the Lee–Carter variants.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:rpstxx:v:73:y:2019:i:1:p:119-138
    DOI: 10.1080/00324728.2018.1545918
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    Citations

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    Cited by:

    1. Aburto, José Manuel & Basellini, Ugofilippo & Baudisch, Annette & Villavicencio, Francisco, 2022. "Drewnowski’s index to measure lifespan variation: Revisiting the Gini coefficient of the life table," Theoretical Population Biology, Elsevier, vol. 148(C), pages 1-10.
    2. Jos'e Manuel Aburto & Ugofilippo Basellini & Annette Baudisch & Francisco Villavicencio, 2021. "Drewnowski's index to measure lifespan variation: Revisiting the Gini coefficient of the life table," Papers 2111.11256, arXiv.org.
    3. Ahbab Mohammad Fazle Rabbi & Stefano Mazzuco, 2021. "Mortality Forecasting with the Lee–Carter Method: Adjusting for Smoothing and Lifespan Disparity," European Journal of Population, Springer;European Association for Population Studies, vol. 37(1), pages 97-120, March.
    4. Ugofilippo Basellini & Søren Kjærgaard & Carlo Giovanni Camarda, 2020. "An age-at-death distribution approach to forecast cohort mortality," Working Papers axafx5_3agsuwaphvlfk, French Institute for Demographic Studies.
    5. Marco Bonetti & Ugofilippo Basellini, 2021. "Epilocal: A real-time tool for local epidemic monitoring," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 44(12), pages 307-332.
    6. Ainhoa-Elena Léger & Stefano Mazzuco, 2021. "What Can We Learn from the Functional Clustering of Mortality Data? An Application to the Human Mortality Database," European Journal of Population, Springer;European Association for Population Studies, vol. 37(4), pages 769-798, November.
    7. 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.
    8. 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.
    9. Marie-Pier Bergeron-Boucher & Søren Kjærgaard & James E. Oeppen & James W. Vaupel, 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.
    10. Catalina Bolancé & Montserrat Guillen, 2021. "Nonparametric Estimation of Extreme Quantiles with an Application to Longevity Risk," Risks, MDPI, vol. 9(4), pages 1-23, April.
    11. 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.
    12. 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.
    13. Carlo G. Camarda & Ugofilippo Basellini, 2021. "Smoothing, Decomposing and Forecasting Mortality Rates," European Journal of Population, Springer;European Association for Population Studies, vol. 37(3), pages 569-602, July.
    14. Basellini, Ugofilippo & Kjærgaard, Søren & Camarda, Carlo Giovanni, 2020. "An age-at-death distribution approach to forecast cohort mortality," Insurance: Mathematics and Economics, Elsevier, vol. 91(C), pages 129-143.

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