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Neighbouring Prediction For Mortality

Author

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  • Wang, Chou-Wen
  • Zhang, Jinggong
  • Zhu, Wenjun

Abstract

We propose a new neighbouring prediction model for mortality forecasting. For each mortality rate at age x in year t, mx,t, we construct an image of neighbourhood mortality data around mx,t, that is, Ꜫmx,t (x1, x2, s), which includes mortality information for ages in [x-x1, x+x2], lagging k years (1 ≤ k ≤ s). Combined with the deep learning model – convolutional neural network, this framework is able to capture the intricate nonlinear structure in the mortality data: the neighbourhood effect, which can go beyond the directions of period, age, and cohort as in classic mortality models. By performing an extensive empirical analysis on all the 41 countries and regions in the Human Mortality Database, we find that the proposed models achieve superior forecasting performance. This framework can be further enhanced to capture the patterns and interactions between multiple populations.

Suggested Citation

  • Wang, Chou-Wen & Zhang, Jinggong & Zhu, Wenjun, 2021. "Neighbouring Prediction For Mortality," ASTIN Bulletin, Cambridge University Press, vol. 51(3), pages 689-718, September.
    • Handle: RePEc:cup:astinb:v:51:y:2021:i:3:p:689-718_1
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    Cited by:

    1. Miguel Santolino, 2023. "Should Selection of the Optimum Stochastic Mortality Model Be Based on the Original or the Logarithmic Scale of the Mortality Rate?," Risks, MDPI, vol. 11(10), pages 1-21, September.
    2. Thilini Dulanjali Kularatne & Jackie Li & Yanlin Shi, 2022. "Forecasting Mortality Rates with a Two-Step LASSO Based Vector Autoregressive Model," Risks, MDPI, vol. 10(11), pages 1-23, November.
    3. Hung-Tsung Hsiao & Chou-Wen Wang & I.-Chien Liu & Ko-Lun Kung, 2024. "Mortality improvement neural-network models with autoregressive effects," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 49(2), pages 363-383, April.
    4. Yang Qiao & Chou-Wen Wang & Wenjun Zhu, 2024. "Machine learning in long-term mortality forecasting," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 49(2), pages 340-362, April.

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