2016/05/31 by Man Chung Fung, Gareth W. Peters, Pavel V. Shevchenko
Decision Sciences · Economics, Econometrics and Finance · Health Professions · Mathematics · Social Sciences · #Algorithm #Artificial intelligence #Bayesian inference #Bayesian probability #Computer science #Econometrics #Global Health Care Issues #Heteroscedasticity #Inference #Insurance, Mortality, Demography, Risk Management #Kalman filter #Markov chain Monte Carlo #Mathematics #Monte Carlo method #Particle filter #State space #State-space representation #Statistical inference #Statistics #Stochastic volatility #Volatility (finance) #demographic modeling and climate adaptation #q-fin.ST #stat.AP
paper · pdf · doi:10.1017/s1748499517000069
published as Annals of Actuarial Science 11 (2), pp. 343-389, 2017 · 46 pages
arxiv created 2016/05/31 · openalex publication_date 2017/05/22 · arxiv updated 2020/08/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Abstract This paper explores and develops alternative statistical representations and estimation approaches for dynamic mortality models. The framework we adopt is to reinterpret popular mortality models such as the Lee–Carter class of models in a general state-space modelling methodology, which allows modelling, estimation and forecasting of mortality under a unified framework. We propose alternative model identification constraints which are more suited to statistical inference in filtering and parameter estimation. We then develop a class of Bayesian state-space models which incorporate a priori beliefs about the mortality model characteristics as well as for more flexible and appropriate assumptions relating to heteroscedasticity that present in observed mortality data. To study long-term mortality dynamics, we introduce stochastic volatility to the period effect. The estimation of the resulting stochastic volatility model of mortality is performed using a recent class of Monte Carlo procedure known as the class of particle Markov chain Monte Carlo methods. We illustrate the framework using Danish male mortality data, and show that incorporating heteroscedasticity and stochastic volatility markedly improves model fit despite an increase of model complexity. Forecasting properties of the enhanced models are examined with long-term and short-term calibration periods on the reconstruction of life tables.