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Projecting UK mortality by using Bayesian generalized additive models
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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), 68 (1). pp. 29-49. doi:10.1111/rssc.12299 ISSN 0035-9254.
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Official URL: http://dx.doi.org/10.1111/rssc.12299
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.
| Item Type: | Journal Article | ||||||||
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| Subjects: | C Auxiliary Sciences of History > CB History of civilization H Social Sciences > HB Economic Theory Q Science > QA Mathematics |
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| Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||||||
| Library of Congress Subject Headings (LCSH): | Mortality -- Forecasting, Mortality -- Forecasting -- Statistical methods, Bayesian statistical decision theory, Mortality -- Statistics | ||||||||
| Journal or Publication Title: | Journal of the Royal Statistical Society: Series C (Applied Statistics) | ||||||||
| Publisher: | Wiley | ||||||||
| ISSN: | 0035-9254 | ||||||||
| Official Date: | 19 December 2018 | ||||||||
| Dates: |
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| Volume: | 68 | ||||||||
| Number: | 1 | ||||||||
| Page Range: | pp. 29-49 | ||||||||
| DOI: | 10.1111/rssc.12299 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||||||
| Date of first compliant deposit: | 28 January 2020 | ||||||||
| Date of first compliant Open Access: | 28 January 2020 | ||||||||
| RIOXX Funder/Project Grant: |
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