Forecasting Bilateral Refugee Flows with High-dimensional Data and Machine Learning Techniques

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

No 1387, Working Papers from Barcelona School of Economics

Abstract: We develop monthly refugee flow forecasting models for 150 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 them out-of-sample, we find that an ensemble forecast combining Random Forest and Extreme Gradient Boosting algorithms consistently outperforms for forecast horizons between 3 to 12 months. For large refugee flow corridors, this holds in a parsimonious model exclusively based on Google Trends variables, which has the advantage of close-to-real-time availability. We provide practical recommendations about how our approach can enable ahead-of-period refugee forecasting applications.

Keywords: learning; forecasting; European Union; refugee flows; asylum seekers; machine; Google trends (search for similar items in EconPapers)
JEL-codes: C53 C55 F22 (search for similar items in EconPapers)
Date: 2023-03
New Economics Papers: this item is included in nep-big, nep-cmp, nep-for and nep-mig
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Persistent link: https://EconPapers.repec.org/RePEc:bge:wpaper:1387

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