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01685cas a2200229 a 4500 |
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AALejournalIMF021342 |
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|c 5.00 USD
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|z 9781513561660
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|a 1018-5941
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|a BD-DhAAL
|c BD-DhAAL
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| 245 |
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|a UnFEAR :
|b Unsupervised Feature Extraction Clustering with an Application to Crisis Regimes Classification.
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|a Washington, D.C. :
|b International Monetary Fund,
|c 2020.
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| 300 |
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|a 1 online resource (24 pages)
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|a IMF Working Papers
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|a <strong>Off-Campus Access:</strong> No User ID or Password Required
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|a <strong>On-Campus Access:</strong> No User ID or Password Required
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|a Electronic access restricted to authorized BRAC University faculty, staff and students
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|a 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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|a Mode of access: Internet
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|a IMF Working Papers; Working Paper ;
|v No. 2020/262
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| 856 |
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|z Full text available on IMF
|u http://elibrary.imf.org/view/journals/001/2020/262/001.2020.issue-262-en.xml
|z IMF e-Library
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