2018
DOI: 10.1017/s1748499518000131
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Cohort effects in mortality modelling: a Bayesian state-space approach

Abstract: Cohort effects are important factors in determining the evolution of human mortality for certain countries. Extensions of dynamic mortality models with cohort features have been proposed in the literature to account for these factors under the generalised linear modelling framework. In this paper we approach the problem of mortality modelling with cohort factors incorporated through a novel formulation under a state-space methodology. In the process we demonstrate that cohort factors can be formulated naturall… Show more

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Cited by 7 publications
(1 citation statement)
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“…After fitting the model, the time and cohort components are forecast using standard Box-Jenkins (ARIMA) procedures. To overcome the identifiability issues related to the estimation of stochastic mortality models, some studies suggest estimating the models as state space models (see, for example, Fung et al, 2017Fung et al, , 2019. Another issue of stochastic mortality models is related to their inability to model the age pattern of mortality decline in developed countries which is decelerating at younger ages and accelerating at older ages.…”
Section: Introductionmentioning
confidence: 99%
“…After fitting the model, the time and cohort components are forecast using standard Box-Jenkins (ARIMA) procedures. To overcome the identifiability issues related to the estimation of stochastic mortality models, some studies suggest estimating the models as state space models (see, for example, Fung et al, 2017Fung et al, , 2019. Another issue of stochastic mortality models is related to their inability to model the age pattern of mortality decline in developed countries which is decelerating at younger ages and accelerating at older ages.…”
Section: Introductionmentioning
confidence: 99%