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Forecasting of cohort fertility under a hierarchical Bayesian approach

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  • Joanne Ellison
  • Erengul Dodd
  • Jonathan J. Forster
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.

Suggested Citation

  • Joanne Ellison & Erengul Dodd & Jonathan J. Forster, 2020. "Forecasting of cohort fertility under a hierarchical Bayesian approach," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 829-856, June.
    • Handle: RePEc:bla:jorssa:v:183:y:2020:i:3:p:829-856
    DOI: 10.1111/rssa.12566
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    References listed on IDEAS

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    Cited by:

    1. Ewa Batyra & Tiziana Leone & Mikko Myrskylä, 2021. "Forecasting of cohort fertility by educational level in countries with limited data availability: the case of Brazil," MPIDR Working Papers WP-2021-011, Max Planck Institute for Demographic Research, Rostock, Germany.
    2. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    3. Yaser Awad & Shaul K. Bar-Lev & Udi Makov, 2022. "A New Class of Counting Distributions Embedded in the Lee–Carter Model for Mortality Projections: A Bayesian Approach," Risks, MDPI, vol. 10(6), pages 1-17, May.
    4. Batyra, Ewa & Leone, Tiziana & Myrskylä, Mikko, 2022. "Forecasting of cohort fertility by educational level in countries with limited data availability: the case of Brazil," LSE Research Online Documents on Economics 116627, London School of Economics and Political Science, LSE Library.

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