UnFEAR : Unsupervised Feature Extraction Clustering with an Application to Crisis Regimes Classification.
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 t...
| Format: | Journal |
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| Language: | English |
| Published: |
Washington, D.C. :
International Monetary Fund,
2020.
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| Series: | IMF Working Papers; Working Paper ;
No. 2020/262 |
| Online Access: | Full text available on IMF |
| 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. |
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| Item Description: | <strong>Off-Campus Access:</strong> No User ID or Password Required <strong>On-Campus Access:</strong> No User ID or Password Required |
| Physical Description: | 1 online resource (24 pages) |
| Format: | Mode of access: Internet |
| ISSN: | 1018-5941 |
| Access: | Electronic access restricted to authorized BRAC University faculty, staff and students |