UnFEAR: Unsupervised Feature Extraction Clustering with an Application to Crisis Regimes Classification
24 Pages Posted: 18 Feb 2021
Date Written: November 25, 2020
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.
Keywords: clustering, unsupervised feature extraction, autoencoder, machine learning, deep learning, biased label problem, crisis prediction
JEL Classification: C1, C45, G1
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