Analysis of the Stochasticity of Mortality Using Variance Decomposition

Ekheden, Erland; Hössjer, Ola

doi:10.1007/978-3-319-06653-0_13

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“…Also this critique is addressed in the present paper. Other modelling approaches that discuss this issue are given in Brouhns et al (2002); Ekheden & Hössjer (2014.…”

Section: Introductionmentioning

confidence: 99%

Mortality forecasting using a Lexis-based state-space model

Andersson

Lindholm

2020

Ann. actuar. sci.

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A new method of forecasting mortality is introduced. The method is based on the continuous-time dynamics of the Lexis diagram, which given weak assumptions implies that the death count data are Poisson distributed. The underlying mortality rates are modelled with a hidden Markov model (HMM) which enables a fully likelihood-based inference. Likelihood inference is done by particle filter methods, which avoids approximating assumptions and also suggests natural model validation measures. The proposed model class contains as special cases many previous models with the important difference that the HMM methods make it possible to estimate the model efficiently. Another difference is that the population and latent variable variability can be explicitly modelled and estimated. Numerical examples show that the model performs well and that inefficient estimation methods can severely affect forecasts.

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“…Also this critique is addressed in the present paper. Other modelling approaches that discuss this issue are given in Brouhns et al (2002); Ekheden & Hössjer (2014.…”

Section: Introductionmentioning

confidence: 99%