Markov-Switching Bayesian Vector Autoregression Model in Mortality Forecasting

Wanying Fu (), Barry R. Smith, Patrick Brewer and Sean Droms
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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

Risks, 2023, vol. 11, issue 9, 1-23

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.

Keywords: MSBVAR; BVAR; regime-switching; mortality forecast; parameter projection; mortality structural change (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2023
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