This paper aims at providing policymakers with a set of early warning indicators helpful in guiding decisions on when to activate macroprudential tools targeting excessive credit growth and leverage. To robustly select the key indicators we apply the “Random Forest” method, which bootstraps and aggregates a multitude of decision trees. On these identified key indicators we grow a binary classification tree which derives the associated optimal early warning thresholds. By using credit to GDP gaps, credit to GDP ratios and credit growth rates, as well as real estate variables in addition to a set of other conditioning variables, the model is designed to not only predict banking crises, but also to give an indication on which macro-prudential policy instrument would be best suited to address specific vulnerabilities.
Banking crises decision trees early warning systems macroprudential policy random forest