TY - JOUR
T1 - Multi-population mortality projection
T2 - the augmented common factor model with structural breaks
AU - Wang, Pengjie
AU - Pantelous, Athanasios A.
AU - Vahid, Farshid
N1 - Publisher Copyright:
© 2021 International Institute of Forecasters
PY - 2023/1
Y1 - 2023/1
N2 - Multi-population mortality forecasting has become an increasingly important area in actuarial science and demography, as a means to avoid long-run divergence in mortality projections. This paper aims to establish a unified state-space Bayesian framework to model, estimate, and forecast mortality rates in a multi-population context. In this regard, we reformulate the augmented common factor model to account for structural/trend changes in the mortality indexes. We conduct a Bayesian analysis to make inferences and generate forecasts so that process and parameter uncertainties can be considered simultaneously and appropriately. We illustrate the efficiency of our methodology through two case studies. Both point and probabilistic forecast evaluations are considered in the empirical analysis. The derived results support the fact that the incorporation of stochastic drifts mitigates the impact of the structural changes in the time indexes on mortality projections.
AB - Multi-population mortality forecasting has become an increasingly important area in actuarial science and demography, as a means to avoid long-run divergence in mortality projections. This paper aims to establish a unified state-space Bayesian framework to model, estimate, and forecast mortality rates in a multi-population context. In this regard, we reformulate the augmented common factor model to account for structural/trend changes in the mortality indexes. We conduct a Bayesian analysis to make inferences and generate forecasts so that process and parameter uncertainties can be considered simultaneously and appropriately. We illustrate the efficiency of our methodology through two case studies. Both point and probabilistic forecast evaluations are considered in the empirical analysis. The derived results support the fact that the incorporation of stochastic drifts mitigates the impact of the structural changes in the time indexes on mortality projections.
KW - Augmented common factor (ACF) model
KW - Bayesian forecasting
KW - Bayesian statistics
KW - Multi-population mortality projection
KW - Structural/trend change
UR - http://www.scopus.com/inward/record.url?scp=85123678852&partnerID=8YFLogxK
U2 - 10.1016/j.ijforecast.2021.12.008
DO - 10.1016/j.ijforecast.2021.12.008
M3 - Article
AN - SCOPUS:85123678852
SN - 0169-2070
VL - 39
SP - 450
EP - 469
JO - International Journal of Forecasting
JF - International Journal of Forecasting
IS - 1
ER -