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Empirical errors and predicted errors in fertility, mortality and migration forecasts in the European Economic Area

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We analyse empirical errors observed in historical population forecasts produced by statistical agencies in 14 European countries since 1950. The focus is on forecasts for three demographic variables: fertility (Total Fertility Rate - TFR), mortality (life expectancy at birth), and migration (net migration). We inspect forecast bias and forecast accuracy in the historical forecasts, as well as the distribution of the errors. Finally, we analyse for each of the three variables correlation patterns in forecast errors across countries and, for mortality, the correlation between errors for men and women. In the second part of the report we use time series model to construct prediction intervals to 2050 for the TFR, the life expectancy for men and women, and net migration in 18 European countries. GARCH models are used for fertility and mortality, while net migration is modelled as an autoregressive process

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  • 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.
  • Handle: RePEc:ssb:dispap:386
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    File URL: https://www.ssb.no/a/publikasjoner/pdf/DP/dp386.pdf
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    1. Gita Persand & Chris Brooks & Simon P. Burke, 2003. "Multivariate GARCH models: software choice and estimation issues," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(6), pages 725-734.
    2. Engle, Robert F. & Kroner, Kenneth F., 1995. "Multivariate Simultaneous Generalized ARCH," Econometric Theory, Cambridge University Press, vol. 11(1), pages 122-150, February.
    3. Chesnais, Jean-Claude, 1992. "The Demographic Transition: Stages, Patterns, and Economic Implications," OUP Catalogue, Oxford University Press, number 9780198286592.
    4. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    5. repec:cai:popine:popu_p1989_44n6_1158 is not listed on IDEAS
    6. Shlyakhter, Alexander I. & Kammen, Daniel M. & Broido, Claire L. & Wilson, Richard, 1994. "Quantifying the credibility of energy projections from trends in past data : The US energy sector," Energy Policy, Elsevier, vol. 22(2), pages 119-130, February.
    7. Roel Jennissen, 2003. "Economic Determinants of Net International Migration in Western Europe," European Journal of Population, Springer;European Association for Population Studies, vol. 19(2), pages 171-198, June.
    8. Thai-Thanh Dang & Pablo Antolín & Howard Oxley, 2001. "Fiscal Implications of Ageing: Projections of Age-Related Spending," OECD Economics Department Working Papers 305, OECD Publishing.
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    Cited by:

    1. 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.
    2. 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.
    3. Alho, Juha, 2008. "Aggregation across countries in stochastic population forecasts," International Journal of Forecasting, Elsevier, vol. 24(3), pages 343-353.
    4. 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.
    5. Anna Matysiak & Beata Nowok, 2007. "Stochastic forecast of the population of Poland, 2005-2050," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 17(11), pages 301-338.
    6. Emily A. Marshall, 2015. "Population Projections and Demographic Knowledge in France and Great Britain in the Postwar Period," Population and Development Review, The Population Council, Inc., vol. 41(2), pages 271-300, June.
    7. Bailey Fosdick & Adrian E. Raftery, 2014. "Regional probabilistic fertility forecasting by modeling between-country correlations," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 30(35), pages 1011-1034.
    8. Rebecca F. Gleditsch & Adrian F. Rogne & Astri Syse & Michael Thomas, 2021. "The accuracy of Statistics Norway’s national population projections," Discussion Papers 948, Statistics Norway, Research Department.
    9. Nico Keilman, 2008. "European Demographic Forecasts Have Not Become More Accurate Over the Past 25 Years," Population and Development Review, The Population Council, Inc., vol. 34(1), pages 137-153, March.
    10. Christina Bohk-Ewald & Marcus Ebeling & Roland Rau, 2017. "Lifespan Disparity as an Additional Indicator for Evaluating Mortality Forecasts," Demography, Springer;Population Association of America (PAA), vol. 54(4), pages 1559-1577, August.

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    More about this item

    Keywords

    stochastic population forecast; empirical forecast errors; prediction intervals; GARCHmodels; TFR; life expectancy; net migration; EEA;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • J11 - Labor and Demographic Economics - - Demographic Economics - - - Demographic Trends, Macroeconomic Effects, and Forecasts

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