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

Principal Investigator(s):  View help for Principal Investigator(s) Konstantin Boss, UAB & BSE; Andre Groeger, UAB, BSE, MOVE; Tobias Heidland, Kiel Institute for the World Economy & Kiel University; Conghan Zheng, UAB & BSE; Finja Krueger, Kiel Institute for the World Economy (IfW), Kiel University, and Institute for the Study of Labor (IZA)

Version:  View help for Version V3

Name File Type Size Last Modified
  CODE 08/05/2024 10:58:AM
  DATA 05/28/2024 09:37:AM
  OUTPUT 05/28/2024 09:12:AM
GTI_codebook.csv text/csv 32.5 KB 05/28/2024 05:25:AM
README.txt text/plain 2.3 KB 08/05/2024 07:00:AM
toc_monthly.csv text/csv 264.2 KB 05/28/2024 04:51:AM

Project Citation: 

Boss, Konstantin, Groeger, Andre , Heidland, Tobias, Zheng, Conghan , and Krueger, Finja . Forecasting bilateral asylum seeker flows with high-dimensional data and machine learning techniques. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2024-08-05. https://doi.org/10.3886/E198322V3

Project Description

Summary:  View help for Summary 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.

Scope of Project

Subject Terms:  View help for Subject Terms migration; forecasting; machine learning; google trends


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Published Versions

V3 [2024-08-05]

V2 [2024-08-05]

V1 [2024-05-28]

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