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Spotting the Danger Zone: Forecasting Financial Crises With Classification Tree Ensembles and Many Predictors
Author(s) -
Ward Felix
Publication year - 2017
Publication title -
journal of applied econometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.878
H-Index - 99
eISSN - 1099-1255
pISSN - 0883-7252
DOI - 10.1002/jae.2525
Subject(s) - spotting , sample (material) , computer science , warning system , econometrics , decision tree , cover (algebra) , financial crisis , artificial intelligence , machine learning , economics , engineering , macroeconomics , mechanical engineering , telecommunications , chemistry , chromatography
Summary This paper introduces classification tree ensembles (CTEs) to the banking crisis forecasting literature. I show that CTEs substantially improve out‐of‐sample forecasting performance over best‐practice early‐warning systems. CTEs enable policymakers to correctly forecast 80% of crises with a 20% probability of incorrectly forecasting a crisis. These findings are based on a long‐run sample (1870–2011), and two broad post‐1970 samples which together cover almost all known systemic banking crises. I show that the marked improvement in forecasting performance results from the combination of many classification trees into an ensemble, and the use of many predictors. Copyright © 2016 John Wiley & Sons, Ltd.