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An Expert-Based Framework for Probabilistic National Population Projections: The Example of Austria

Author

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  • Wolfgang Lutz

    (Applied Systems Analysis (IIASA))

  • Sergei Scherbov

    (University of Groningen)

Abstract

The traditional way of dealing with uncertainty in population projections through high and low variants is unsatisfactory because it remains unclear what range of uncertainty these alternative paths are assumed to cover. But probabilistic approaches have not yet found their way into official population projections. This paper proposes an expert-based probabilistic approach that seems to meet important criteria for successful application to national and international projections: 1) it provides significant advantages to current practice, 2) it presents an evolution of current practice rather than a discontinuity, 3) it is scientifically sound, and 4) it is applicable to all countries. In a recent Nature article (Lutz et al., 1997) this method was applied to 13 world regions. This paper discusses the applicability to national projections by directly taking the alternative assumptions defined by the Austrian Statistical Office. Sensitivity analyses that resolve some methodological questions about the approach are also presented.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:eurpop:v:14:y:1998:i:1:d:10.1023_a:1006040321755
    DOI: 10.1023/A:1006040321755
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    References listed on IDEAS

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    1. Alho, Juha M., 1990. "Stochastic methods in population forecasting," International Journal of Forecasting, Elsevier, vol. 6(4), pages 521-530, December.
    2. Lee, Ronald D., 1993. "Modeling and forecasting the time series of US fertility: Age distribution, range, and ultimate level," International Journal of Forecasting, Elsevier, vol. 9(2), pages 187-202, August.
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    Citations

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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. Han Lin Shang, 2012. "Point and interval forecasts of age-specific life expectancies," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 27(21), pages 593-644.
    3. Stefan Rayer & Stanley Smith & Jeff Tayman, 2009. "Empirical Prediction Intervals for County Population Forecasts," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 28(6), pages 773-793, December.
    4. Tom Wilson & Martin Bell, 2004. "Australia's uncertain demographic future," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 11(8), pages 195-234.
    5. Warren Sanderson & Sergei Scherbov & Brian O'Neill & Wolfgang Lutz, 2003. "Conditional Probabilistic Population Forecasting," VID Working Papers 0303, Vienna Institute of Demography (VID) of the Austrian Academy of Sciences in Vienna.
    6. 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.
    7. Han Lin Shang, 2012. "Point and interval forecasts of age-specific fertility rates: a comparison of functional principal component methods," Monash Econometrics and Business Statistics Working Papers 10/12, Monash University, Department of Econometrics and Business Statistics.
    8. Wolfgang P. Lutz, 2001. "World population in 2050: assessing the projections: discussion," Conference Series ; [Proceedings], Federal Reserve Bank of Boston, vol. 46.
    9. Dunstan Kim & Ball Christopher, 2016. "Demographic Projections: User and Producer Experiences of Adopting a Stochastic Approach," Journal of Official Statistics, Sciendo, vol. 32(4), pages 947-962, December.

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