Spotting the Danger Zone: Forecasting Financial Crises With Classification Tree Ensembles and Many Predictors
引入分类树集成方法预测银行危机，相比传统预警系统大幅提升预测能力，能以20%误报率正确预测80%的危机，基于1870-2011年长期样本和1970年后两个广泛样本。
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.