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Markov-Switching Bayesian Vector Autoregression Model in Mortality Forecasting

Author

Listed:
  • Wanying Fu

    (Mathematics Department, Lebanon Valley College, Annville, PA 17003, USA)

  • Barry R. Smith

    (Mathematics Department, Lebanon Valley College, Annville, PA 17003, USA)

  • Patrick Brewer

    (Mathematics Department, Lebanon Valley College, Annville, PA 17003, USA)

  • Sean Droms

    (Mathematics Department, Lebanon Valley College, Annville, PA 17003, USA)

Abstract

We apply a Markov-switching Bayesian vector autoregression (MSBVAR) model to mortality forecasting. MSBVAR has not previously been applied in this context, and our results show that it is a promising tool for mortality forecasting. Our model shows better forecasting accuracy than the Lee–Carter and Bayesian vector autoregressive (BVAR) models without regime-switching and while retaining the advantages of BVAR. MSBVAR provides more reliable estimates for parameter uncertainty and more flexibility in the shapes of point-forecast curves and shapes of confidence intervals than BVAR. Through regime-switching, MSBVAR helps to capture transitory changes in mortality and provides insightful quantitative information about mortality dynamics.

Suggested Citation

  • Wanying Fu & Barry R. Smith & Patrick Brewer & Sean Droms, 2023. "Markov-Switching Bayesian Vector Autoregression Model in Mortality Forecasting," Risks, MDPI, vol. 11(9), pages 1-23, August.
    • Handle: RePEc:gam:jrisks:v:11:y:2023:i:9:p:152-:d:1222432
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    References listed on IDEAS

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