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Predictive Intervals for Age-Specific Fertility

Author

Listed:
  • Nico Keilman

    (University of Oslo)

  • Dinh Quang Pham

    (University of Oslo)

Abstract

A multivariate ARIMA model is combined with a Gammacurve to predict confidence intervals for age-specificbirth rates by one-year age groups. The method isapplied to observed age-specific births in Norwaybetween 1900 and 1995, and predictive intervals arecomputed for each year up to 2050. The predictedtwo-thirds confidence intervals for Total Fertility(TF) around 2010 agree well with TF errors in oldpopulation forecasts made by Statistics Norway. Themethod gives useful predictions for age-specificfertility up to the years 2020–2030. For later years,the intervals become too wide. Methods which do nottake account of estimation errors in the ARIMA modelcoefficients underestimate the uncertainty for futureTF values. The findings suggest that the marginbetween high and low fertility variants in officialpopulation forecasts for many Western countries aretoo narrow.

Suggested Citation

  • Nico Keilman & Dinh Quang Pham, 2000. "Predictive Intervals for Age-Specific Fertility," European Journal of Population, Springer;European Association for Population Studies, vol. 16(1), pages 41-65, March.
    • Handle: RePEc:spr:eurpop:v:16:y:2000:i:1:d:10.1023_a:1006385413134
    DOI: 10.1023/A:1006385413134
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    References listed on IDEAS

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    1. Wolfgang Lutz & Sergei Scherbov, 1998. "An Expert-Based Framework for Probabilistic National Population Projections: The Example of Austria," European Journal of Population, Springer;European Association for Population Studies, vol. 14(1), pages 1-17, March.
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    Cited by:

    1. Booth, Heather, 2006. "Demographic forecasting: 1980 to 2005 in review," International Journal of Forecasting, Elsevier, vol. 22(3), pages 547-581.
    2. Pengkun Wu & Qing Wu & Yudan Dou, 2017. "Simulating population development under new fertility policy in China based on system dynamics model," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(5), pages 2171-2189, September.
    3. Hyndman, Rob J. & Booth, Heather, 2008. "Stochastic population forecasts using functional data models for mortality, fertility and migration," International Journal of Forecasting, Elsevier, vol. 24(3), pages 323-342.
    4. Rueda, Cristina & Rodríguez, Pilar, 2010. "State space models for estimating and forecasting fertility," International Journal of Forecasting, Elsevier, vol. 26(4), pages 712-724, October.
    5. Yang, Yang & Shang, Han Lin & Raymer, James, 2024. "Forecasting Australian fertility by age, region, and birthplace," International Journal of Forecasting, Elsevier, vol. 40(2), pages 532-548.
    6. Nico Keilman & Dinh Quang Pham & Arve Hetland, 2002. "Why population forecasts should be probabilistic - illustrated by the case of Norway," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 6(15), pages 409-454.
    7. Ortega, Jose Antonio & Poncela, Pilar, 2005. "Joint forecasts of Southern European fertility rates with non-stationary dynamic factor models," International Journal of Forecasting, Elsevier, vol. 21(3), pages 539-550.
    8. Голубков В.В. & Яковец Т.Ю., 2018. "Прогноз Демографической Ситуации В России До 2033 Г," Журнал Экономика и математические методы (ЭММ), Центральный Экономико-Математический Институт (ЦЭМИ), vol. 54(4), pages 71-87, октябрь.
    9. Vanella, Patrizio, 2017. "Stochastische Prognose demografischer Komponenten auf Basis der Hauptkomponentenanalyse," Hannover Economic Papers (HEP) dp-597, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.

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