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Journal of the Royal Statistical Society Series A (Statistics in Society)
Article . 2025 . Peer-reviewed
License: CC BY NC
Data sources: Crossref
https://doi.org/10.2139/ssrn.4...
Article . 2024 . Peer-reviewed
Data sources: Crossref
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Asylum-seekers at the extremes

Authors: Noori, Mohammad; Bee, Marco;

Asylum-seekers at the extremes

Abstract

Abstract This article conducts a first-hand study on forecasting asylum-seekers’ applications across seven major host countries, i.e. the USA, Germany, France, Italy, Spain, Austria, and Greece by 2027. Poisson regression is used for flow forecasting and Extreme Value Theory for the maximum monthly number of applications. As for the latter, we first employ a static version of the Peaks-over-Threshold approach, before modelling the possible non-stationarity of the exceedances via a Generalized Pareto Distribution with time-varying parameters. Overall, the findings are in line with one another and can provide a useful road map to policymakers.

Country
Italy
Related Organizations
Keywords

extremes, forecasting, migration, refugees, statistical modelling

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green
hybrid