Stochastic Mortality Modeling: Key Drivers and Dependent Residuals

DOI10.1080/10920277.2017.1286992zbMath1414.91219OpenAlexW2605528838MaRDI QIDQ5379229

George Mavros, George Streftaris, Torsten Kleinow, Andrew J. G. Cairns

Publication date: 28 May 2019

Published in: North American Actuarial Journal (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/10920277.2017.1286992

zbMATH Keywords

stochastic mortality ratesmultivariate autoregressive residuals model

Mathematics Subject Classification ID

Related Items (4)

A class of random field memory models for mortality forecasting ⋮ Modelling mortality: A bayesian factor-augmented var (favar) approach ⋮ Bayesian Poisson log-bilinear models for mortality projections with multiple populations ⋮ Longevity risk and capital markets: the 2019--20 update

Uses Software

• WinBUGS
• BayesDA

Cites Work

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• Modelling residuals dependence in dynamic life tables: a geostatistical approach
• A Poisson log-bilinear regression approach to the construction of projected lifetables.
• Lee-Carter mortality forecasting with age-specific enhancement.
• Modelling dependent data for longevity projections
• A cohort-based extension to the Lee-Carter model for mortality reduction factors
• Bayesian Modeling Using WinBUGS
• Identifiability, Improper Priors, and Gibbs Sampling for Generalized Linear Models
• A Quantitative Comparison of Stochastic Mortality Models Using Data From England and Wales and the United States
• Bootstrapping the Poisson log-bilinear model for mortality forecasting

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