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Forecasting of cohort fertility under a hierarchical Bayesian approach
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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). pp. 829-856. doi:10.1111/rssa.12566 ISSN 1467-985X.
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Official URL: http://dx.doi.org/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.
Item Type: | Journal Article | ||||||||
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||||||
Journal or Publication Title: | Journal of the Royal Statistical Society Series A: Statistics in Society | ||||||||
Publisher: | Oxford University Press | ||||||||
ISSN: | 1467-985X | ||||||||
Official Date: | June 2020 | ||||||||
Dates: |
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Volume: | 183 | ||||||||
Number: | 3 | ||||||||
Page Range: | pp. 829-856 | ||||||||
DOI: | 10.1111/rssa.12566 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Free Access (unspecified licence, 'bronze OA') |
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