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Forecasting bilateral asylum seeker flows with high-dimensional data and machine learning techniques

Author

Listed:
  • Konstantin Boss
  • Andre Groeger
  • Tobias Heidland
  • Finja Krueger
  • Conghan Zheng

Abstract

We develop monthly asylum seeker flow forecasting models for 157 origin countries to the EU27, using machine learning and high-dimensional data, including digital trace data from Google Trends. Comparing different models and forecasting horizons and validating out-of-sample, we find that an ensemble forecast combining Random Forest and Extreme Gradient Boosting algorithms outperforms the random walk over horizons between 3 and 12 months. For large corridors, this holds in a parsimonious model exclusively based on Google Trends variables, which has the advantage of near real-time availability. We provide practical recommendations how our approach can enable ahead-of-period asylum seeker flow forecasting applications.

Suggested Citation

  • Konstantin Boss & Andre Groeger & Tobias Heidland & Finja Krueger & Conghan Zheng, 2025. "Forecasting bilateral asylum seeker flows with high-dimensional data and machine learning techniques," Journal of Economic Geography, Oxford University Press, vol. 25(1), pages 3-19.
    • Handle: RePEc:oup:jecgeo:v:25:y:2025:i:1:p:3-19.
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    File URL: http://hdl.handle.net/10.1093/jeg/lbae023
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    More about this item

    Keywords

    forecasting; refugee migration; asylum seeker; mixed migration; European Union; machine learning; Google Trends;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • F22 - International Economics - - International Factor Movements and International Business - - - International Migration

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