Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/249887 
Year of Publication: 
2021
Series/Report no.: 
ECB Working Paper No. 2614
Publisher: 
European Central Bank (ECB), Frankfurt a. M.
Abstract: 
We develop early warning models for financial crisis prediction by applying machine learning techniques to macrofinancial data for 17 countries over 1870-2016. Most nonlin-ear machine learning models outperform logistic regression in out-of-sample predictions and forecasting. We identify economic drivers of our machine learning models using a novel framework based on Shapley values, uncovering nonlinear relationships between the predic-tors and crisis risk. Throughout, the most important predictors are credit growth and the slope of the yield curve, both domestically and globally. A flat or inverted yield curve is of most concern when nominal interest rates are low and credit growth is high.
Subjects: 
machine learning
financial stability
financial crises
credit growth
yield curve
Shapley values
JEL: 
C40
C53
E44
F30
G01
Persistent Identifier of the first edition: 
ISBN: 
978-92-899-4867-8
Document Type: 
Working Paper

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