Forecasting bilateral asylum seeker flows with high-dimensional data and machine learning techniques
Bibliographic Data
| ID | 12695181 |
|---|---|
| Authors | Konstantin Boss (Department of Economics and Economic History, Universitat Autònoma de Barcelona (UAB) , 08193 Bellaterra, corresponding author), André Gröger (0000-0002-1482-9392, Department of Economics and Economic History, Universitat Autònoma de Barcelona (UAB) , 08193 Bellaterra), André Groeger (Universitat Autonòma de Barcelona), Tobias Heidland (0000-0001-9646-1132, Kiel Institute for the World Economy (IfW) , 24105 Kiel), Finja Krueger (Kiel Institute for the World Economy (IfW) , 24105 Kiel), Conghan Zheng (Department of Economics and Economic History, Universitat Autònoma de Barcelona (UAB) , 08193 Bellaterra) |
| Year | 2024 |
| Volume | 25 |
| Issue | 1 |
| Pages | 3-19 |
| Publication date | 2024-08-10 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Economic Geography (JOURNAL) |
| Journal identifiers | ISSN: 1468-2702 • E-ISSN: 1468-2710 |
| Publisher | Oxford University Press (OUP) (PUBLISHER) |
| DOI | 10.1093/jeg/lbae023 |
| OpenAlex | W4401487911 |
| Language | EN |
| Citations received | 1 |
| References cited | 19 |
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
Boosting (machine learning · Econometrics · Economics · Ensemble learning · Gradient boosting · Machine learning · Random forest · Random walk · Statistics · Census and Population Estimation · Computer Science · Mathematics · Migration and Labor Dynamics · Migration, Refugees, and Integration · Artificial Intelligence
Why Has U.S. Inflation Become Harder to Forecast?
Refugees, Asylum Seekers, and Policy in OECD Countries
Prediction Policy Problems
Forecasting Using Principal Components From a Large Number of Predictors
Greedy function approximation
Random Forests
Native language, spoken language, translation and trade
Predicting Risk Factors Associated with Forced Migration
Searching for a better life
Toward an Early Warning System for Monitoring Asylum-Related Migration Flows in Europe
Leveraging Facebook's Advertising Platform to Monitor Stocks of Migrants
| Citations per year | 1 |
|---|---|
| Citation span | 2026 - 2026 (1) |
| Highly cited | No |