Rotation in age patterns of mortality decline: statistical evidence and modeling

Johnny Siu Hang Li, Joseph H.T. Kim

Research output: Contribution to journalArticlepeer-review

Abstract

In the context of mortality forecasting, rotation refers to the phenomenon that mortality decline accelerates at older ages but decelerates at younger ages. Since rotation is typically subtle, it is difficult to be confirmed and modeled in a statistical, data-driven manner. In this paper, we attempt to overcome this challenge by proposing an alternative modeling approach. The approach encompasses a new model structure, which includes a component that is devoted to measuring rotation. It also features a modeling technique known as ANCOVA, which allows us to statistically detect rotation and extrapolate the phenomenon into the future. Our proposed approach yields plausible mortality forecasts that are similar to those produced by Li et al. [Extending the Lee-Carter method to model the rotation of age patterns of mortality decline for long-term projections. Demography 50 (6), 2037-205, and may be considered more advantageous than the approach of Li et al. in the sense that it is able to generate not only static but also stochastic forecasts.

Original languageEnglish
Pages (from-to)621-652
Number of pages32
JournalProbability in the Engineering and Informational Sciences
Volume37
Issue number2
DOIs
Publication statusPublished - 2023 Apr 1

Bibliographical note

Publisher Copyright:
© The Author(s), 2023. Published by Cambridge University Press.

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

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