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

• Authors: Konstantin Boss, Andre Groeger, Tobias Heidland, Finja Krueger and Conghan Zheng.
• Journal of Economic Geography, Vol. 25, No. 1, 3–19, January 2025.

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.