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Bayesian Mixture Modelling for Mortality Projection

Author

Listed:
  • Jackie Li

    (Department of Actuarial Studies and Business Analytics, Macquarie University, Macquarie Park, NSW 2109, Australia)

  • Atsuyuki Kogure

    (Faculty of Business Administration, Tokyo Keizai University, Tokyo 185-8502, Japan)

Abstract

Although a large number of mortality projection models have been proposed in the literature, relatively little attention has been paid to a formal assessment of the effect of model uncertainty. In this paper, we construct a Bayesian framework for embedding more than one mortality projection model and utilise the finite mixture model concept to allow for the blending of model structures. Under this framework, the varying features of different model structures can be exploited jointly and coherently to have a more detailed description of the underlying mortality patterns. We show that the proposed Bayesian approach performs well in fitting and forecasting Japanese mortality.

Suggested Citation

  • Jackie Li & Atsuyuki Kogure, 2021. "Bayesian Mixture Modelling for Mortality Projection," Risks, MDPI, vol. 9(4), pages 1-12, April.
    • Handle: RePEc:gam:jrisks:v:9:y:2021:i:4:p:76-:d:536535
    as

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    References listed on IDEAS

    as
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    Full references (including those not matched with items on IDEAS)

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