Spotting the Danger Zone - Forecasting Financial Crises with Classification Tree Ensembles and Many Predictors

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Bonn Econ Discussion Papers ; No. 01/2014

Classification and Topics

• Classification: Wirtschaft ; Forecasting Models; Simulation Methods ; Monetary Policy, Central Banking, and the Supply of Money and Credit: General ; Financial Crises ; Economic History: Macroeconomics and Monetary Economics; Industrial Structure; Growth; Fluctuations: General, International, or Comparative

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Ward, Felix
• Event: Veröffentlichung
• (who): University of Bonn, Bonn Graduate School of Economics (BGSE)
• (where): Bonn
• (when): 2014

Further information

• Handle: http://hdl.handle.net/10419/128613
• Last update: 10.03.2025, 11:43 AM CET