Bayesian mixture modelling for mortality projection

Jackie Li; Atsuyuki Kogure

doi:10.3390/risks9040076

Bayesian mixture modelling for mortality projection

Jackie Li, Atsuyuki Kogure

Department of Actuarial Studies and Business Analytics

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

4 Downloads (Pure)

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.

Original language	English
Article number	76
Pages (from-to)	1-12
Number of pages	12
Journal	Risks
Volume	9
Issue number	4
DOIs	https://doi.org/10.3390/risks9040076
Publication status	Published - 15 Apr 2021

Bibliographical note

Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

finite mixture model
Bayesian modelling
Lee–Carter model
Cairns-Blade-Dowd model
age-cohort model

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10.3390/risks9040076Licence: CC BY

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KW - Cairns-Blade-Dowd model

KW - age-cohort model

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Bayesian mixture modelling for mortality projection

Abstract

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Keywords

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