UnFEAR: Unsupervised Feature Extraction Clustering with an Application to Crisis Regimes Classification
Author/Editor: Jorge A Chan-Lau, Ran Wang
Release Date: © November, 2020
ISBN
: 978-1-51356-166-0
Stock #: WPIEA2020262
English
Stock Status: On back-order
Languages and formats available
English | French | Spanish | Arabic | Russian | Chinese | Portuguese | |
Paperback | Yes | ||||||
Yes | |||||||
ePub | Yes |
Description
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.
More publications in this series: Working Papers
More publications by: Jorge A Chan-Lau ; Ran Wang