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Stochastic modelling and projection of mortality improvements using a hybrid parametric/semiparametric age-period-cohort model

Stochastic modelling and projection of mortality improvements using a hybrid parametric/semiparametric age-period-cohort model
Stochastic modelling and projection of mortality improvements using a hybrid parametric/semiparametric age-period-cohort model
We propose a comprehensive and coherent approach for mortality projection using a maximum likelihood method which benefits from full use of the substantial data available on mortality rates, their improvement rates, and the associated variability. Under this approach we fit a negative binomial distribution to overcome one of the several limitations of existing approaches such as insufficiently robust mortality projections as a result of employing a model (e.g. Poisson) which provides a poor fit to the data. We also impose smoothness in parameter series which vary over age, cohort, and time in an integrated way. Generalised Additive Models (GAMs), being a flexible class of semiparametric statistical models, allow us to differentially smooth components, such as cohorts, more heavily in areas of sparse data for the component concerned. While GAMs can provide a reasonable fit for the ages where there is adequate data, estimation and extrapolation of mortality rates using a GAM at higher ages is problematic due to high variation in crude rates. At these ages, parametric models can give a more robust fit, enabling a borrowing of strength across age groups. Our projection methodology assumes a smooth transition between a GAM at lower ages and a fully parametric model at higher ages.
University of Southampton
Dodd, Erengul
b3faed76-f22b-4928-a922-7f0b8439030d
Forster, Jonathan
e3c534ad-fa69-42f5-b67b-11617bc84879
Bijak, Jakub
e33bf9d3-fca6-405f-844c-4b2decf93c66
Smith, Peter W.F.
961a01a3-bf4c-43ca-9599-5be4fd5d3940
Dodd, Erengul
b3faed76-f22b-4928-a922-7f0b8439030d
Forster, Jonathan
e3c534ad-fa69-42f5-b67b-11617bc84879
Bijak, Jakub
e33bf9d3-fca6-405f-844c-4b2decf93c66
Smith, Peter W.F.
961a01a3-bf4c-43ca-9599-5be4fd5d3940

Dodd, Erengul, Forster, Jonathan, Bijak, Jakub and Smith, Peter W.F. (2018) Stochastic modelling and projection of mortality improvements using a hybrid parametric/semiparametric age-period-cohort model University of Southampton 24pp.

Record type: Monograph (Working Paper)

Abstract

We propose a comprehensive and coherent approach for mortality projection using a maximum likelihood method which benefits from full use of the substantial data available on mortality rates, their improvement rates, and the associated variability. Under this approach we fit a negative binomial distribution to overcome one of the several limitations of existing approaches such as insufficiently robust mortality projections as a result of employing a model (e.g. Poisson) which provides a poor fit to the data. We also impose smoothness in parameter series which vary over age, cohort, and time in an integrated way. Generalised Additive Models (GAMs), being a flexible class of semiparametric statistical models, allow us to differentially smooth components, such as cohorts, more heavily in areas of sparse data for the component concerned. While GAMs can provide a reasonable fit for the ages where there is adequate data, estimation and extrapolation of mortality rates using a GAM at higher ages is problematic due to high variation in crude rates. At these ages, parametric models can give a more robust fit, enabling a borrowing of strength across age groups. Our projection methodology assumes a smooth transition between a GAM at lower ages and a fully parametric model at higher ages.

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Mortality WP Pure
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Published date: 2018

Identifiers

Local EPrints ID: 422219
URI: https://eprints.soton.ac.uk/id/eprint/422219
PURE UUID: 23d801a4-f587-4104-92f3-ce50f38ade4c
ORCID for Jonathan Forster: ORCID iD orcid.org/0000-0002-7867-3411
ORCID for Jakub Bijak: ORCID iD orcid.org/0000-0002-2563-5040
ORCID for Peter W.F. Smith: ORCID iD orcid.org/0000-0003-4423-5410

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Date deposited: 19 Jul 2018 16:30
Last modified: 24 May 2019 00:38

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