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Forecasting of cohort fertility under a hierarchical Bayesian approach

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
0964-1998
829-856
Ellison, Joanne
d1560ac9-2c6c-49e8-b5c4-aa2258624e97
Dodd, Erengul
b3faed76-f22b-4928-a922-7f0b8439030d
Forster, Jonathan
e3c534ad-fa69-42f5-b67b-11617bc84879
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), 829-856. (doi:10.1111/rssa.12566).

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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

Identifiers

Local EPrints ID: 437813
URI: http://eprints.soton.ac.uk/id/eprint/437813
ISSN: 0964-1998
PURE UUID: db6a105b-b6ce-45e7-b641-f973ce1ca7bd
ORCID for Joanne Ellison: ORCID iD orcid.org/0000-0002-6973-8797
ORCID for Erengul Dodd: ORCID iD orcid.org/0000-0001-6658-0990
ORCID for Jonathan Forster: ORCID iD orcid.org/0000-0002-7867-3411

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Date deposited: 19 Feb 2020 17:30
Last modified: 18 Jul 2024 04:01

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Contributors

Author: Joanne Ellison ORCID iD
Author: Erengul Dodd ORCID iD
Author: Jonathan Forster ORCID iD

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