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Evaluating Probabilistic Population Forecasts

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  • Nico Keilman

Abstract

[eng] Statisticians have developed scoring rules for evaluating probabilistic forecasts against observations. However, there are very few applications in the literature on population forecasting. A scoring rule measures the distance between the predictive distribution and its outcome. We review scoring rules that reward accuracy (the outcome is close to the expectation of the distribution) and sharpness (the distribution has low variance, which makes it difficult to hit the target). We evaluate probabilistic population forecasts for France, the Netherlands, and Norway. Forecasts for total population size for the Netherlands and for Norway performed quite well. The error in the jump-off population caused a bad score for the French forecast. We evaluate the age and sex composition predicted for the year 2010. The predictions for the Netherlands received the best scores, except for the oldest old. The age pattern for the Norwegian score reflects the under-prediction of immigration after the enlargement of the European Union in 2005.

Suggested Citation

  • Nico Keilman, 2020. "Evaluating Probabilistic Population Forecasts," Economie et Statistique / Economics and Statistics, Institut National de la Statistique et des Etudes Economiques (INSEE), issue 520-521, pages 49-64.
    • Handle: RePEc:nse:ecosta:ecostat_2020_520d_4
    DOI: https://doi.org/10.24187/ecostat.2020.520d.2033
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    References listed on IDEAS

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    1. Ross Askanazi & Francis X. Diebold & Frank Schorfheide & Minchul Shin, 2018. "On the Comparison of Interval Forecasts," Journal of Time Series Analysis, Wiley Blackwell, vol. 39(6), pages 953-965, November.
    2. James E. Matheson & Robert L. Winkler, 1976. "Scoring Rules for Continuous Probability Distributions," Management Science, INFORMS, vol. 22(10), pages 1087-1096, June.
    3. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
    4. 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.
    5. Jakub Bijak & John Bryant, 2016. "Bayesian demography 250 years after Bayes," Population Studies, Taylor & Francis Journals, vol. 70(1), pages 1-19, March.
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    Cited by:

    1. Barigou, Karim & Goffard, Pierre-Olivier & Loisel, Stéphane & Salhi, Yahia, 2023. "Bayesian model averaging for mortality forecasting using leave-future-out validation," International Journal of Forecasting, Elsevier, vol. 39(2), pages 674-690.
    2. Nathalie Blanpain, 2020. "Is the Ageing of the French Population Unavoidable?," Economie et Statistique / Economics and Statistics, Institut National de la Statistique et des Etudes Economiques (INSEE), issue 520-521, pages 65-85.

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    More about this item

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • J11 - Labor and Demographic Economics - - Demographic Economics - - - Demographic Trends, Macroeconomic Effects, and Forecasts

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