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

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  • Mr. Jorge A Chan-Lau

Abstract

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.

Suggested Citation

  • Mr. Jorge A Chan-Lau, 2020. "UnFEAR: Unsupervised Feature Extraction Clustering with an Application to Crisis Regimes Classification," IMF Working Papers 2020/262, International Monetary Fund.
    • Handle: RePEc:imf:imfwpa:2020/262
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