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Assumptions for long-term stochastic population forecasts in 18 European countries

Author

Listed:
  • Maarten Alders

    (Statistics Netherlands)

  • Nico Keilman

    (University of Oslo)

  • Harri Cruijsen

    (DEMOCAST)

Abstract

The aim of the ‘Uncertain Population of Europe’(UPE) project was to compute long-term stochastic (probabilistic) population forecasts for 18 European countries. We developed a general methodology for constructing predictive distributions for fertility, mortality and migration. The assumptions underlying stochastic population forecasts can be assessed by analysing errors in past forecasts or model-based estimates of forecast errors, or by expert judgement. All three approaches have been used in the project. This article summarizes and discusses the results of the three approaches. It demonstrates how the—sometimes conflicting—results can be synthesized into a consistent set of assumptions about the expected levels and the uncertainty of total fertility rate, life expectancy at birth of men and women, and net migration for 18 European countries.

Suggested Citation

  • Maarten Alders & Nico Keilman & Harri Cruijsen, 2007. "Assumptions for long-term stochastic population forecasts in 18 European countries," European Journal of Population, Springer;European Association for Population Studies, vol. 23(1), pages 33-69, March.
    • Handle: RePEc:spr:eurpop:v:23:y:2007:i:1:d:10.1007_s10680-006-9104-4
    DOI: 10.1007/s10680-006-9104-4
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    References listed on IDEAS

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    1. Chesnais, Jean-Claude, 1992. "The Demographic Transition: Stages, Patterns, and Economic Implications," OUP Catalogue, Oxford University Press, number 9780198286592, Decembrie.
    2. Nico Keilman & Dinh Quang Pham, 2004. "Empirical errors and predicted errors in fertility, mortality and migration forecasts in the European Economic Area," Discussion Papers 386, Statistics Norway, Research Department.
    3. Wolfgang Lutz & Warren Sanderson & Sergei Scherbov, 2001. "The end of world population growth," Nature, Nature, vol. 412(6846), pages 543-545, August.
    4. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    5. 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.
    6. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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    Citations

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    Cited by:

    1. Gianni Corsetti & Marco Marsili, 2013. "Previsioni stocastiche della popolazione nell’ottica di un Istituto Nazionale di Statistica," Rivista di statistica ufficiale, ISTAT - Italian National Institute of Statistics - (Rome, ITALY), vol. 15(2-3), pages 5-29.
    2. Tomáš Sobotka, 2008. "Overview Chapter 7: The rising importance of migrants for childbearing in Europe," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 19(9), pages 225-248.
    3. Lenny Stoeldraijer & Coen van Duin & Leo van Wissen & Fanny Janssen, 2013. "Impact of different mortality forecasting methods and explicit assumptions on projected future life expectancy: The case of the Netherlands," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 29(13), pages 323-354.
    4. Mónica Borunda & Katya Rodríguez-Vázquez & Raul Garduno-Ramirez & Javier de la Cruz-Soto & Javier Antunez-Estrada & Oscar A. Jaramillo, 2020. "Long-Term Estimation of Wind Power by Probabilistic Forecast Using Genetic Programming," Energies, MDPI, vol. 13(8), pages 1-24, April.
    5. Alho, Juha, 2008. "Aggregation across countries in stochastic population forecasts," International Journal of Forecasting, Elsevier, vol. 24(3), pages 343-353.
    6. Fanny Janssen & Leo Wissen & Anton Kunst, 2013. "Including the Smoking Epidemic in Internationally Coherent Mortality Projections," Demography, Springer;Population Association of America (PAA), vol. 50(4), pages 1341-1362, August.
    7. Francesco Billari & Rebecca Graziani & Eugenio Melilli, 2014. "Stochastic Population Forecasting Based on Combinations of Expert Evaluations Within the Bayesian Paradigm," Demography, Springer;Population Association of America (PAA), vol. 51(5), pages 1933-1954, October.
    8. Laurent Toulemon, 2007. "Projections démographiques pour la France et ses régions : vieillissement de la population et stabilisation de la population active," Économie et Statistique, Programme National Persée, vol. 408(1), pages 81-94.
    9. 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.
    10. Nico Keilman, 2017. "A combined Brass-random walk approach to probabilistic household forecasting: Denmark, Finland, and the Netherlands, 2011–2041," Journal of Population Research, Springer, vol. 34(1), pages 17-43, March.
    11. Vanella, Patrizio, 2017. "Age- and Sex-Specific Fertility in Germany until the Year 2040 - The Impact of International Migration," Hannover Economic Papers (HEP) dp-606, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    12. Raftery, Adrian E. & Ševčíková, Hana, 2023. "Probabilistic population forecasting: Short to very long-term," International Journal of Forecasting, Elsevier, vol. 39(1), pages 73-97.
    13. Vanella, Patrizio & Deschermeier, Philipp, 2018. "A Principal Component Simulation of Age-Specific Fertility - Impacts of Family and Social Policy on Reproductive Behavior in Germany," Hannover Economic Papers (HEP) dp-630, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    14. Heinz Stefan, 2014. "Uncertainty quantification of world population growth: A self-similar PDF model," Monte Carlo Methods and Applications, De Gruyter, vol. 20(4), pages 261-277, December.
    15. Leontine Alkema & Adrian Raftery & Patrick Gerland & Samuel Clark & François Pelletier & Thomas Buettner & Gerhard Heilig, 2011. "Probabilistic Projections of the Total Fertility Rate for All Countries," Demography, Springer;Population Association of America (PAA), vol. 48(3), pages 815-839, August.
    16. Solveig Christiansen & Nico Keilman, 2013. "Probabilistic household forecasts based on register data- the case of Denmark and Finland," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 28(43), pages 1263-1302.
    17. Tom Wilson & Fiona Shalley, 2019. "Subnational population forecasts: Do users want to know about uncertainty?," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 41(13), pages 367-392.

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