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Mortality forecasting using a modified Continuous Mortality Investigation Mortality Projections Model for China I: methodology and country-level results

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  • Huang, Fei
  • Browne, Bridget

Abstract

In this paper, we project future mortality rates for actuarial use with Chinese data using a modified Continuous Mortality Investigation (CMI) Mortality Projections Model. The model adopts a convergence structure from “initial†to “long-term†rates of mortality improvement as the process of projection. The initial rates of mortality improvement are derived using two-dimensional P-spline methodology. Given the short history of Chinese data, the long-term rates of mortality improvement are determined by borrowing information from international experience. K-means clustering with dynamic time warping distance is used to classify populations, which is novel in the actuarial mortality research field. The original CMI approach is deterministic, however, in this paper we make it stochastic using techniques outlined by Koller and described by Browne et al. Comparing our results with a pure extrapolative approach, we find that the CMI Mortality Projections Model is more suitable for long-term projections for China.

Suggested Citation

  • Huang, Fei & Browne, Bridget, 2017. "Mortality forecasting using a modified Continuous Mortality Investigation Mortality Projections Model for China I: methodology and country-level results," Annals of Actuarial Science, Cambridge University Press, vol. 11(1), pages 20-45, March.
  • Handle: RePEc:cup:anacsi:v:11:y:2017:i:01:p:20-45_00
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    Cited by:

    1. Hong Li & Yang Lu & Pintao Lyu, 2021. "Coherent Mortality Forecasting for Less Developed Countries," Risks, MDPI, vol. 9(9), pages 1-21, August.
    2. Apostolos Bozikas & Georgios Pitselis, 2019. "Credible Regression Approaches to Forecast Mortality for Populations with Limited Data," Risks, MDPI, vol. 7(1), pages 1-22, February.
    3. Qian Lu & Katja Hanewald & Xiaojun Wang, 2021. "Subnational Mortality Modelling: A Bayesian Hierarchical Model with Common Factors," Risks, MDPI, vol. 9(11), pages 1-21, November.
    4. Carlo Giovanni Camarda, 2019. "Smooth constrained mortality forecasting," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 41(38), pages 1091-1130.

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