Forecasting of cohort fertility under a hierarchical Bayesian approach
Forecasting of cohort fertility under a hierarchical Bayesian approach
Fertility projections are a key determinant of population forecasts, which are widely used by government policy makers and planners. In keeping with the recent literature, we propose an intuitive and transparent hierarchical Bayesian model to forecast cohort fertility. Using Hamiltonian Monte Carlo methods and a data set from the human fertility database, we obtain fertility forecasts for 30 countries. We use scoring rules to assess the predictive accuracy of the forecasts quantitatively; these indicate that our model predicts with an accuracy comparable with that of the best-performing models in the current literature overall, with stronger performance for countries without a recent structural shift. Our findings support the position of hierarchical Bayesian modelling at the forefront of population forecasting methods.
Cohort fertility, Forecasting, Hamiltonian Monte Carlo methods, Hierarchical Bayesian models, Human fertility database, Scoring rules
829-856
Ellison, Joanne
d1560ac9-2c6c-49e8-b5c4-aa2258624e97
Dodd, Erengul
b3faed76-f22b-4928-a922-7f0b8439030d
Forster, Jonathan
e3c534ad-fa69-42f5-b67b-11617bc84879
June 2020
Ellison, Joanne
d1560ac9-2c6c-49e8-b5c4-aa2258624e97
Dodd, Erengul
b3faed76-f22b-4928-a922-7f0b8439030d
Forster, Jonathan
e3c534ad-fa69-42f5-b67b-11617bc84879
Ellison, Joanne, Dodd, Erengul and Forster, Jonathan
(2020)
Forecasting of cohort fertility under a hierarchical Bayesian approach.
Journal of the Royal Statistical Society: Series A (Statistics in Society), 183 (3), .
(doi:10.1111/rssa.12566).
Abstract
Fertility projections are a key determinant of population forecasts, which are widely used by government policy makers and planners. In keeping with the recent literature, we propose an intuitive and transparent hierarchical Bayesian model to forecast cohort fertility. Using Hamiltonian Monte Carlo methods and a data set from the human fertility database, we obtain fertility forecasts for 30 countries. We use scoring rules to assess the predictive accuracy of the forecasts quantitatively; these indicate that our model predicts with an accuracy comparable with that of the best-performing models in the current literature overall, with stronger performance for countries without a recent structural shift. Our findings support the position of hierarchical Bayesian modelling at the forefront of population forecasting methods.
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Accepted/In Press date: 14 February 2020
e-pub ahead of print date: 17 April 2020
Published date: June 2020
Additional Information:
This is the accepted version of the following article: Ellison, Joanne, Dodd, Erengul and Forster, Jonathan J (2020) Forecasting of cohort fertility under a hierarchical Bayesian approach. Journal of the Royal Statistical Society: Series A (Statistics in Society), 183 (3), 829-856, which has been published in final form at DOI: doi:10.1111/rssa.12566. This article may be used for non-commercial purposes in accordance with the Wiley Self-Archiving Policy [http://www.wileyauthors.com/self-archiving].
Keywords:
Cohort fertility, Forecasting, Hamiltonian Monte Carlo methods, Hierarchical Bayesian models, Human fertility database, Scoring rules
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Local EPrints ID: 437813
URI: http://eprints.soton.ac.uk/id/eprint/437813
ISSN: 0964-1998
PURE UUID: db6a105b-b6ce-45e7-b641-f973ce1ca7bd
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Date deposited: 19 Feb 2020 17:30
Last modified: 08 May 2024 04:01
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Jonathan Forster
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