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Bayesian forecasting of immigration to selected European countries by using expert knowledge

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  • Jakub Bijak
  • Arkadiusz Wiśniowski
Abstract
Summary. The aim of the paper is to present Bayesian forecasts of immigration for seven European countries to 2025, based on quantitative data and qualitative knowledge elicited from country‐specific migration experts in a two‐round Delphi survey. In line with earlier results, most of the immigration processes under study were found to be barely predictable in the long run, exhibiting non‐stationary features. This outcome was obtained largely irrespectively of the expert knowledge input, which nevertheless was found useful in describing the predictive uncertainty, especially in the short term. It is argued that, under the non‐stationarity of migration processes, too long forecasts horizons are inadequate, which is a serious challenge for population forecasts in general.

Suggested Citation

  • Jakub Bijak & Arkadiusz Wiśniowski, 2010. "Bayesian forecasting of immigration to selected European countries by using expert knowledge," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(4), pages 775-796, October.
    • Handle: RePEc:bla:jorssa:v:173:y:2010:i:4:p:775-796
    DOI: 10.1111/j.1467-985X.2009.00635.x
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    References listed on IDEAS

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    1. Susi Gorbey & Doug James & Jacques Poot, 1999. "Population Forecasting with Endogenous Migration: An Application to Trans-Tasman Migration," International Regional Science Review, , vol. 22(1), pages 69-101, April.
    2. 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.
    3. Rowe, Gene & Wright, George, 1999. "The Delphi technique as a forecasting tool: issues and analysis," International Journal of Forecasting, Elsevier, vol. 15(4), pages 353-375, October.
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    1. Frans Willekens, 2018. "Towards causal forecasting of international migration," Vienna Yearbook of Population Research, Vienna Institute of Demography (VID) of the Austrian Academy of Sciences in Vienna, vol. 16(1), pages 199-218.
    2. Bernard Baffour & James Raymer, 2019. "Estimating multiregional survivorship probabilities for sparse data: An application to immigrant populations in Australia, 1981–2011," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 40(18), pages 463-502.
    3. Demirel, Duygun Fatih & Basak, Melek, 2019. "A fuzzy bi-level method for modeling age-specific migration," Socio-Economic Planning Sciences, Elsevier, vol. 68(C).
    4. Willekens Frans, 2019. "Evidence-Based Monitoring of International Migration Flows in Europe," Journal of Official Statistics, Sciendo, vol. 35(1), pages 231-277, March.
    5. Tongzheng Pu & Chongxing Huang & Jingjing Yang & Ming Huang, 2023. "Transcending Time and Space: Survey Methods, Uncertainty, and Development in Human Migration Prediction," Sustainability, MDPI, vol. 15(13), pages 1-23, July.
    6. Shang, Han Lin & Smith, Peter W.F. & Bijak, Jakub & Wiśniowski, Arkadiusz, 2016. "A multilevel functional data method for forecasting population, with an application to the United Kingdom," International Journal of Forecasting, Elsevier, vol. 32(3), pages 629-649.
    7. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    8. Aral, Mustafa M., 2020. "Knowledge based analysis of continental population and migration dynamics," Technological Forecasting and Social Change, Elsevier, vol. 151(C).
    9. 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.
    10. Heidland, Tobias & Jannsen, Nils & Groll, Dominik & Kalweit, René & Boockmann, Bernhard, 2021. "Analyse und Prognose von Migrationsbewegungen," Kieler Beiträge zur Wirtschaftspolitik 34, Kiel Institute for the World Economy (IfW Kiel).
    11. Wiśniowski Arkadiusz & Bijak Jakub & Christiansen Solveig & Forster Jonathan J. & Keilman Nico & Raymer James & Smith Peter W.F., 2013. "Utilising Expert Opinion to Improve the Measurement of International Migration in Europe," Journal of Official Statistics, Sciendo, vol. 29(4), pages 583-607, December.

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