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Projecting UK mortality by using Bayesian generalized additive models

Projecting UK mortality by using Bayesian generalized additive models
Projecting UK mortality by using Bayesian generalized additive models

Forecasts of mortality provide vital information about future populations, with implications for pension and healthcare policy as well as for decisions made by private companies about life insurance and annuity pricing. The paper presents a Bayesian approach to the forecasting of mortality that jointly estimates a generalized additive model (GAM) for mortality for the majority of the age range and a parametric model for older ages where the data are sparser. The GAM allows smooth components to be estimated for age, cohort and age-specific improvement rates, together with a non-smoothed period effect. Forecasts for the UK are produced by using data from the human mortality database spanning the period 1961–2013. A metric that approximates predictive accuracy is used to estimate weights for the ‘stacking’ of forecasts from models with different points of transition between the GAM and parametric elements. Mortality for males and females is estimated separately at first, but a joint model allows the asymptotic limit of mortality at old ages to be shared between sexes and furthermore provides for forecasts accounting for correlations in period innovations.

Age-Period-Cohort, Bayesian analysis, Forecasting, Generalized Additive Models, Mortality
0035-9254
Hilton, Jason
da31e515-1e34-4e9f-846d-633176bb3931
Dodd, Erengul
b3faed76-f22b-4928-a922-7f0b8439030d
Forster, Jonathan J.
e3c534ad-fa69-42f5-b67b-11617bc84879
Smith, Peter W.F.
961a01a3-bf4c-43ca-9599-5be4fd5d3940
Hilton, Jason
da31e515-1e34-4e9f-846d-633176bb3931
Dodd, Erengul
b3faed76-f22b-4928-a922-7f0b8439030d
Forster, Jonathan J.
e3c534ad-fa69-42f5-b67b-11617bc84879
Smith, Peter W.F.
961a01a3-bf4c-43ca-9599-5be4fd5d3940

Hilton, Jason, Dodd, Erengul, Forster, Jonathan J. and Smith, Peter W.F. (2018) Projecting UK mortality by using Bayesian generalized additive models. Journal of the Royal Statistical Society. Series C: Applied Statistics. (doi:10.1111/rssc.12299).

Record type: Article

Abstract

Forecasts of mortality provide vital information about future populations, with implications for pension and healthcare policy as well as for decisions made by private companies about life insurance and annuity pricing. The paper presents a Bayesian approach to the forecasting of mortality that jointly estimates a generalized additive model (GAM) for mortality for the majority of the age range and a parametric model for older ages where the data are sparser. The GAM allows smooth components to be estimated for age, cohort and age-specific improvement rates, together with a non-smoothed period effect. Forecasts for the UK are produced by using data from the human mortality database spanning the period 1961–2013. A metric that approximates predictive accuracy is used to estimate weights for the ‘stacking’ of forecasts from models with different points of transition between the GAM and parametric elements. Mortality for males and females is estimated separately at first, but a joint model allows the asymptotic limit of mortality at old ages to be shared between sexes and furthermore provides for forecasts accounting for correlations in period innovations.

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More information

Submitted date: 28 January 2018
Accepted/In Press date: 1 June 2018
e-pub ahead of print date: 12 August 2018
Keywords: Age-Period-Cohort, Bayesian analysis, Forecasting, Generalized Additive Models, Mortality

Identifiers

Local EPrints ID: 417513
URI: https://eprints.soton.ac.uk/id/eprint/417513
ISSN: 0035-9254
PURE UUID: 8f17a8e8-caa2-4c76-bc33-e6add9bbd05d
ORCID for Jason Hilton: ORCID iD orcid.org/0000-0001-9473-757X
ORCID for Peter W.F. Smith: ORCID iD orcid.org/0000-0003-4423-5410

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Date deposited: 01 Feb 2018 17:30
Last modified: 06 Oct 2018 04:03

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