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

Published online by Cambridge University Press:  09 January 2023

Johnny Siu-Hang Li  and
Joseph H.T. Kim Open the ORCID record for Joseph H.T. Kim [Opens in a new window]
Johnny Siu-Hang Li
Affiliation:
Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON N2L 3G1, Canada. E-mail: shli@uwaterloo.ca
Joseph H.T. Kim
Affiliation:
Department of Statistics and Data Science, Yonsei University, Seoul 120-749, Korea. E-mail: jhtkim@yonsei.ac.kr
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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.

Keywords

ANCOVAThe Lee–Carter modelLong-term mortality projectionsStochastic mortality forecasts
Type
Research Article
Information
Probability in the Engineering and Informational Sciences , Volume 37 , Special Issue 2: Probability and stochastic modeling in actuarial science and related fields , April 2023 , pp. 621 - 652
Copyright
© The Author(s), 2023. Published by Cambridge University Press

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