Forecasting banking crises with dynamic panel probit models

Banking crises are rare events, but when they occur their consequences are often dramatic. The aim of this paper is to contribute to the toolkit of early warning models available to policy makers by exploring the dynamics and non-linearities embedded in a panel dataset covering several countries over four decades (from 1970Q1 to 2010Q4). The in-sample and out-of-sample forecast performance of several dynamic probit models is evaluated, with the objective of developing a common vulnerability indicator with early warning properties. The results obtained show that adding dynamic components and exuberance indicators to the models substantially improves the ability to forecast banking crises.