Markov-Switching Bayesian vector autoregression model in mortality forecasting
Wanying Fu, Barry R. Smith, Patrick Brewer and Sean Droms
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
Year of publication: |
2023
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Authors: | Fu, Wanying ; Smith, Barry R. ; Brewer, Patrick ; Droms, Sean |
Published in: |
Risks : open access journal. - Basel : MDPI, ISSN 2227-9091, ZDB-ID 2704357-5. - Vol. 11.2023, 9, Art.-No. 152, p. 1-23
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Subject: | MSBVAR | BVAR | regime-switching | mortality forecast | parameter projection | mortalitystructural change | Sterblichkeit | Mortality | Prognoseverfahren | Forecasting model | VAR-Modell | VAR model | Bayes-Statistik | Bayesian inference | Markov-Kette | Markov chain | Prognose | Forecast | Theorie | Theory |
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