Detection of rare events: A machine learning toolkit with an application to banking crises

We propose a machine learning toolkit applied to the detection of rare events, namely banking crises. For this purpose, we consider a broad set of macroeconomic series (credit-to-GDP gap, house prices, stock prices, inflation rates, long-term and short-term interest rates, etc.), in combination with their leads and lags, various filtering methodologies, and datascience models that complement time series analysis. The main advantages of the approach are its robustness, its flexibility and its prediction performance. Based on the best model specification, our methodology allows to compute an indicator for the probability of banking crisis along with an alert threshold up to 6 quarters ahead in real time for various developed economies.