Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/128613 
Authors: 
Year of Publication: 
2014
Series/Report no.: 
Bonn Econ Discussion Papers No. 01/2014
Publisher: 
University of Bonn, Bonn Graduate School of Economics (BGSE), Bonn
Abstract: 
To improve the detection of the economic ”danger zones” from which severe banking crises emanate, this paper introduces classification tree ensembles to the banking crisis forecasting literature. I show that their out-of-sample performance in forecasting binary banking crisis indicators surpasses current best-practice early warning systems based on logit models by a substantial margin. I obtain this result on the basis of one long-run- (1870-2011), as well as two broad post-1970 macroeconomic panel datasets. I particularly show that two marked improvements in forecasting performance result from the combination of many classification trees into an ensemble, and the use of many predictors.
JEL: 
C53
E50
G01
N10
Document Type: 
Working Paper

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