UnFEAR: Unsupervised Feature Extraction Clustering with an Application to Crisis Regimes Classification

Author/Editor:

Jorge A Chan-Lau ; Ran Wang

Publication Date:

November 25, 2020

Electronic Access:

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Summary:

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.

Series:

Working Paper No. 2020/262

Frequency:

regular

English

Publication Date:

November 25, 2020

ISBN/ISSN:

9781513561660/1018-5941

Stock No:

WPIEA2020262

Format:

Paper

Pages:

24

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