Spotting the Danger Zone: Forecasting Financial Crises With Classification Tree Ensembles and Many Predictors
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.
|Persistent URL||dx.doi.org/10.1002, hdl.handle.net/1765/120836|
|Journal||Journal of Applied Econometrics|
Ward, F.P.L. (2017). Spotting the Danger Zone: Forecasting Financial Crises With Classification Tree Ensembles and Many Predictors. Journal of Applied Econometrics, 32(2). doi:10.1002