Forecasting rare events is a challenge, especially if these events are driven by many different factors and assume different characteristics. We explore the dynamic dimension of discrete choice models to improve the forecasting accuracy of early warning models of systemic banking crises. Our results show that introducing this dynamic component into the models signifi cantly improves the quality of the results.