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An innovation mortality prediction model with cohort effect

Author

Listed:
  • Hongmin Xiao
  • Miaomiao Zhao
  • Xiang Li
  • Aiqin Bai

Abstract

Considering that the cohort effect is added to the population mortality prediction model, the data information on the impact of birth year on mortality can be captured. In this article, the Linear Link model (LL) is extended to obtain the Extended Linear Link model (ELL) with cohort effect. Model fitting and forecasting employ a two-stage approach, first estimating the age term parameters of the extended model, and then forecasting the time parameters using an optimal combination ARIMA(p,d,q) of time series model. Using the mortality data of the Chinese population aged 0–89 from 1995 to 2018 to fit and predict the model, and to test the stability by rolling-window time frame. The results show that when the singular value decomposition method and the least square estimation method are adopted, the prediction effect of the Extended Linear Link model with cohort effect is significantly better than the previous Linear Link model, and the accuracy of fitting and predicting are improved.

Suggested Citation

  • Hongmin Xiao & Miaomiao Zhao & Xiang Li & Aiqin Bai, 2024. "An innovation mortality prediction model with cohort effect," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 53(20), pages 7477-7489, October.
    • Handle: RePEc:taf:lstaxx:v:53:y:2024:i:20:p:7477-7489
    DOI: 10.1080/03610926.2023.2264998
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