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

Jorge Chan-Lau

No 2020/262, IMF Working Papers from International Monetary Fund

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.

Keywords: clustering; unsupervised feature extraction; autoencoder; deep learning; biased label problem; crisis prediction; WP; crisis frequency; crisis observation; crisis risk; crisis data points; machine learning; Early warning systems; Global (search for similar items in EconPapers)
Pages: 24
Date: 2020-11-25
New Economics Papers: this item is included in nep-cmp, nep-ecm and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.imf.org/external/pubs/cat/longres.aspx?sk=49870 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:imf:imfwpa:2020/262

Ordering information: This working paper can be ordered from
http://www.imf.org/external/pubs/pubs/ord_info.htm

Access Statistics for this paper

More papers in IMF Working Papers from International Monetary Fund International Monetary Fund, Washington, DC USA. Contact information at EDIRC.
Bibliographic data for series maintained by Akshay Modi ().