Abstract
AbstractThe addition of a set of cohort parameters to a mortality model can generate complex identifiability issues due to the collinearity between the dimensions of age, period and cohort. These issues can lead to robustness problems and difficulties making projections of future mortality rates. Since many modern mortality models incorporate cohort parameters, we believe that a comprehensive analysis of the identifiability issues in age/period/cohort mortality models is needed. In this paper, we discuss the origin of identifiability issues in general models before applying these insights to simple but commonly used mortality models. We then discuss how to project mortality models so that our forecasts of the future are independent of any arbitrary choices we make when fitting a model to data in order to identify the historical parameters.
Publisher
Cambridge University Press (CUP)
Subject
Statistics, Probability and Uncertainty,Economics and Econometrics,Statistics and Probability
Reference41 articles.
1. Mortality density forecasts: an analysis of six stochastic mortality models;Cairns;Insurance: Mathematics and Economics,2011
2. A General Procedure for Constructing Mortality Models
3. A Bayesian Approach to Modeling and Projecting Cohort Effects
4. Detecting year-of-birth mortality patterns with limited data;Richards;Journal of the Royal Statistical Society: Series A (Statistics in Society),2008
5. A universal pattern of mortality decline in the G7 countries
Cited by
20 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献