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: 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: 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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