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

November 25, 2020

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Format: Chicago

Jorge A Chan-Lau, and Ran Wang. UnFEAR: Unsupervised Feature Extraction Clustering with an Application to Crisis Regimes Classification, (USA: International Monetary Fund, 2020) accessed December 22, 2024

Disclaimer: IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.

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.

Subject: Early warning systems, Economic and financial statistics, Financial crises, Financial statistics, Machine learning, Technology

Keywords: Autoencoder, Biased label problem, Clustering, Crisis data points, Crisis frequency, Crisis observation, Crisis prediction, Crisis risk, Deep learning, Early warning systems, Global, Machine learning, Unsupervised feature extraction, WP

Publication Details

Pages:

24
Volume:

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DOI:

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Issue:

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Series:

Working Paper No. 2020/262
Stock No:

WPIEA2020262
ISBN:

9781513561660
ISSN:

1018-5941

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