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The impact of multiple structural changes on mortality predictions

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  • Frank van Berkum
  • Katrien Antonio
  • Michel Vellekoop

Abstract

Most mortality models proposed in recent literature rely on the standard ARIMA framework (in particular: a random walk with drift) to project mortality rates. As a result the projections are highly sensitive to the calibration period. We therefore analyse the impact of allowing for multiple structural changes on a large collection of mortality models. We find that this may lead to more robust projections for the period effect but that there is only a limited effect on the ranking of the models based on backtesting criteria, since there is often not yet sufficient statistical evidence for structural changes. However, there are cases for which we do find improvements in estimates and we therefore conclude that one should not exclude on beforehand that structural changes may have occurred.

Suggested Citation

  • Frank van Berkum & Katrien Antonio & Michel Vellekoop, 2016. "The impact of multiple structural changes on mortality predictions," Scandinavian Actuarial Journal, Taylor & Francis Journals, vol. 2016(7), pages 581-603, August.
    • Handle: RePEc:taf:sactxx:v:2016:y:2016:i:7:p:581-603
    DOI: 10.1080/03461238.2014.987807
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    Cited by:

    1. Huijing Li & Rui Zhou & Min Ji, 2023. "Nonlinear Modeling of Mortality Data and Its Implications for Longevity Bond Pricing," Risks, MDPI, vol. 11(12), pages 1-25, November.
    2. Barigou, Karim & Goffard, Pierre-Olivier & Loisel, Stéphane & Salhi, Yahia, 2023. "Bayesian model averaging for mortality forecasting using leave-future-out validation," International Journal of Forecasting, Elsevier, vol. 39(2), pages 674-690.

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