Konstantin Boss, Andre Groeger, Tobias Heidland, Finja Krueger, Conghan Zheng
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引用次数: 0
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.
期刊介绍:
The aims of the Journal of Economic Geography are to redefine and reinvigorate the intersection between economics and geography, and to provide a world-class journal in the field. The journal is steered by a distinguished team of Editors and an Editorial Board, drawn equally from the two disciplines. It publishes original academic research and discussion of the highest scholarly standard in the field of ''economic geography'' broadly defined. Submitted papers are refereed, and are evaluated on the basis of their creativity, quality of scholarship, and contribution to advancing understanding of the geographic nature of economic systems and global economic change.