Format:
1 Online-Ressource (circa 25 Seiten)
,
Illustrationen
ISBN:
9781513561660
Series Statement:
IMF working paper WP/20, 262
Content:
We introduce unFEAR, Unsupervised Feature Extraction Clustering, to identify economic crisis regimes. Given labeled crisis and non-crisis episodes and the corresponding features values, unFEAR uses unsupervised representation learning and a novel mode contrastive autoencoder to group episodes into time-invariant non-overlapping clusters, each of which could be identified with a different regime. The likelihood that a country may experience an econmic crisis could be set equal to its cluster crisis frequency. Moreover, unFEAR could serve as a first step towards developing cluster-specific crisis prediction models tailored to each crisis regime
Additional Edition:
Erscheint auch als Druck-Ausgabe UnFEAR: Unsupervised Feature Extraction Clustering with an Application to Crisis Regimes Classification Washington, D.C. : International Monetary Fund, 2020 ISBN 9781513561660
Language:
English
Keywords:
Graue Literatur
DOI:
10.5089/9781513561660.001
