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Gompertz law revisited: Forecasting mortality with a multi-factor exponential model

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  • Li, Hong
  • Tan, Ken Seng
  • Tuljapurkar, Shripad
  • Zhu, Wenjun
Abstract
This paper provides a flexible way to address some ongoing challenges in mortality modeling, with a special focus on the mortality curvature and possible mortality plateau for extremely old ages. In particular, we extend the Gompertz law by proposing a multi-factor exponential model, a framework that is able to capture flexible mortality patterns, and allows for a convenient estimation and prediction algorithm. An extensive empirical analysis is conducted using the proposed framework with a merged mortality database containing a large number of countries and regions with credible old-age mortality data. We find that the proposed exponential model leads to superior goodness-of-fit to historical data, and better out-of-sample forecasting performance. Moreover, the exponential model predicts more balanced mortality improvements across ages, and thus leads to higher projected remaining life expectancy for the old ages than existing Gompertz-based mortality models. Finally, the modeling capacity of the proposed exponential model is further demonstrated by a multi-population extension, and an illustrative example of estimation and forecast is provided.

Suggested Citation

  • Li, Hong & Tan, Ken Seng & Tuljapurkar, Shripad & Zhu, Wenjun, 2021. "Gompertz law revisited: Forecasting mortality with a multi-factor exponential model," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 268-281.
    • Handle: RePEc:eee:insuma:v:99:y:2021:i:c:p:268-281
    DOI: 10.1016/j.insmatheco.2021.03.018
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    5. 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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