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A new relational method for smoothing and projecting age-specific fertility rates: TOPALS

Author

Listed:
  • Joop de Beer

    (Nederlands Interdisciplinair Demografisch Instituut (NIDI))

Abstract

Age-specific fertility rates can be smoothed using parametric models or splines. Alternatively a relational model can be used which relates the age profile to be fitted or projected to a standard age schedule. This paper introduces TOPALS (tool for projecting age patterns using linear splines), a new relational method that is less dependent on the choice of the standard age schedule than previous methods. TOPALS models the relationship between the age-specific fertility rates to be fitted and the standard age schedule by a linear spline. This paper uses TOPALS for smoothing fertility age profiles for 30 European countries. The use of TOPALS to create scenarios of the future level and age pattern of fertility is illustrated by applying the method to project future fertility rates for six European countries.

Suggested Citation

  • Joop de Beer, 2011. "A new relational method for smoothing and projecting age-specific fertility rates: TOPALS," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 24(18), pages 409-454.
    • Handle: RePEc:dem:demres:v:24:y:2011:i:18
    DOI: 10.4054/DemRes.2011.24.18
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    References listed on IDEAS

    as
    1. Booth, Heather, 2006. "Demographic forecasting: 1980 to 2005 in review," International Journal of Forecasting, Elsevier, vol. 22(3), pages 547-581.
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    5. Tomas Frejka & Tomáš Sobotka, 2008. "Overview Chapter 1: Fertility in Europe," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 19(3), pages 15-46.
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    Citations

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    Cited by:

    1. Keilman, Nico, 2016. "Household forecasting: Preservation of age patterns," International Journal of Forecasting, Elsevier, vol. 32(3), pages 726-735.
    2. Bernard Baffour & James Raymer, 2019. "Estimating multiregional survivorship probabilities for sparse data: An application to immigrant populations in Australia, 1981–2011," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 40(18), pages 463-502.
    3. Sigurd Dyrting & Abraham Flaxman & Ethan Sharygin, 2022. "Reconstruction of age distributions from differentially private census data," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 41(6), pages 2311-2329, December.
    4. Pavel Grigoriev & Anatoli I. Michalski & Vasily P. Gorlischev & Dmitri A. Jdanov & Vladimir M. Shkolnikov, 2018. "New methods for estimating detailed fertility schedules from abridged data," MPIDR Working Papers WP-2018-001, Max Planck Institute for Demographic Research, Rostock, Germany.
    5. Jan Weymeirsch & Julian Ernst & Ralf Münnich, 2024. "Model Recalibration for Regional Bias Reduction in Dynamic Microsimulations," Mathematics, MDPI, vol. 12(10), pages 1-25, May.
    6. Arkadiusz Wiśniowski & Peter Smith & Jakub Bijak & James Raymer & Jonathan Forster, 2015. "Bayesian Population Forecasting: Extending the Lee-Carter Method," Demography, Springer;Population Association of America (PAA), vol. 52(3), pages 1035-1059, June.
    7. Sigurd Dyrting, 2020. "Smoothing migration intensities with P-TOPALS," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 43(55), pages 1607-1650.
    8. Alexander, Monica, 2022. "Decomposing dimensions of mortality inequality," SocArXiv uqwxj, Center for Open Science.

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    More about this item

    Keywords

    age-specific fertility rate (ASFR); smoothing; Brass model; relational model; European Union; model age schedules; population scenario;
    All these keywords.

    JEL classification:

    • J1 - Labor and Demographic Economics - - Demographic Economics
    • Z0 - Other Special Topics - - General

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