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

Konstantin Boss, Andre Groeger, Tobias Heidland, Finja Krueger and Conghan Zheng
Authors registered in the RePEc Author Service: André Gröger

Journal of Economic Geography, 2025, vol. 25, issue 1, 3-19

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.

Keywords: forecasting; refugee migration; asylum seeker; mixed migration; European Union; machine learning; Google Trends (search for similar items in EconPapers)
JEL-codes: C53 C55 F22 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1093/jeg/lbae023 (application/pdf)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:oup:jecgeo:v:25:y:2025:i:1:p:3-19.

Ordering information: This journal article can be ordered from
https://academic.oup.com/journals

Access Statistics for this article

Journal of Economic Geography is currently edited by Jorge De la Roca, Stephen Gibbons, Simona Iammarino, Amanda Ross and James Faulconbridge

More articles in Journal of Economic Geography from Oxford University Press Oxford University Press, Great Clarendon Street, Oxford OX2 6DP, UK.
Bibliographic data for series maintained by Oxford University Press ().