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dc.contributor.authorALESSI LUCIAen_GB
dc.contributor.authorDETKEN CARSTENen_GB
dc.date.accessioned2018-04-18T00:21:11Z-
dc.date.available2017-12-05en_GB
dc.date.available2018-04-18T00:21:11Z-
dc.date.created2017-12-04en_GB
dc.date.issued2018en_GB
dc.date.submitted2017-06-01en_GB
dc.identifier.citationJOURNAL OF FINANCIAL STABILITY vol. 35 p. 215-225en_GB
dc.identifier.issn1572-3089en_GB
dc.identifier.urihttp://www.sciencedirect.com/science/article/pii/S1572308917304291en_GB
dc.identifier.urihttp://publications.jrc.ec.europa.eu/repository/handle/JRC106988-
dc.description.abstractUnsustainable credit developments lead to the build-up of systemic risks to financial stability. While this is an accepted truth, how to assess whether risks are getting out of hand remains a challenge. To identify excessive credit growth and aggregate leverage we propose an early warning system, which aims at predicting banking crises. In particular, we use a modern classification tree ensemble technique, the “Random Forest”, and include (global) credit as well as real estate variables as predictors.en_GB
dc.description.sponsorshipJRC.B.1-Finance and Economyen_GB
dc.format.mediumPrinteden_GB
dc.languageENGen_GB
dc.publisherELSEVIER SCIENCE INCen_GB
dc.relation.ispartofseriesJRC106988en_GB
dc.titleIdentifying excessive credit growth and leverageen_GB
dc.typeArticles in periodicals and booksen_GB
dc.identifier.doi10.1016/j.jfs.2017.06.005en_GB
JRC Directorate:Growth and Innovation

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