Rethinking Age-Period-Cohort Mortality Trend Models
Daniel Alai () and
Michael Sherris ()
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Daniel Alai: ARC Centre of Excellence in Population Ageing Research, Australian School of Business, University of New South Wales
Michael Sherris: School of Risk and Actuarial Studies and ARC Centre of Excellence in Population Ageing Research, Australian School of Business, University of New South Wales
No 201212, Working Papers from ARC Centre of Excellence in Population Ageing Research (CEPAR), Australian School of Business, University of New South Wales
Longevity risk arising from uncertain mortality improvement is one of the major risks facing annuity providers and pension funds. In this paper 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 generalized 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.
Keywords: Mortality Modelling; Age-Period-Cohort Models; Generalized Linear Models; Lee-Carter Models (search for similar items in EconPapers)
JEL-codes: G22 G23 C51 C18 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-age, nep-dem, nep-for and nep-hea
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