Skip to main content
Kent Academic Repository

Rethinking Age-Period-Cohort Mortality Trend Models

Alai, Daniel H., Sherris, Michael (2014) Rethinking Age-Period-Cohort Mortality Trend Models. Scandinavian Actuarial Journal, 2014 (3). pp. 208-227. ISSN 0346-1238. (doi:10.1080/03461238.2012.676563) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:38165)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.
Official URL:


Longevity risk arising from uncertain mortality improvement is one of the major risks facing annuity providers and pension funds. In this article, we show how applying trend models from non-life claims reserving to age-period-cohort mortality trends provides new insight in estimating mortality improvement and quantifying its uncertainty. Age, period and cohort trends are modelled with distinct effects for each age, calendar year and birth year in a generalised linear models framework. The effects are distinct in the sense that they are not conjoined with age coefficients, borrowing from regression terminology, we denote them as main effects. Mortality models in this framework for age-period, age-cohort and age-period-cohort effects are assessed using national population mortality data from Norway and Australia to show the relative significance of cohort effects as compared to period effects. Results are compared with the traditional Lee–Carter model. The bilinear period effect in the Lee–Carter model is shown to resemble a main cohort effect in these trend models. However, the approach avoids the limitations of the Lee–Carter model when forecasting with the age-cohort trend model.

Item Type: Article
DOI/Identification number: 10.1080/03461238.2012.676563
Uncontrolled keywords: Mortality Modelling; Age-Period-Cohort Models; Generalised Linear Models; Lee-Carter Models
Subjects: Q Science > QA Mathematics (inc Computing science)
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
Depositing User: Daniel Alai
Date Deposited: 05 Feb 2014 14:32 UTC
Last Modified: 17 Aug 2022 10:56 UTC
Resource URI: (The current URI for this page, for reference purposes)

University of Kent Author Information

  • Depositors only (login required):

Total unique views for this document in KAR since July 2020. For more details click on the image.