|We aim at detecting periods preceding banking crises in the euro area through an early warning system (EWS) and propose a new method to deal with model uncertainty. In a first step, we select a set of macro-financial risk indicators for their signaling ability among a large number of candidates over the period spanning from 1985:q1 to 2009:q4. Then, we run all the possible logit models including four of these indicators as explanatory variables in order to assess the pre-crises probabilities at each time. We retain two sets of models: a small one only including models with all coefficients significant and with the expected signs, and a large set, obtained by relaxing the selection criteria. In a second step, we calculate the weighted average of the pre-crisis probabilities estimated by the models belonging to the two selected sets. The weight given to each model is proportional to its usefulness at identifying pre-crises periods either at the panel or the country-level. The simulations performed both over and out of sample show that aggregating more models yields better results than relying on any single model or only a few of them, as model uncertainty is reduced. Performance is also enhanced by aggregating models’ results with country-specific weights relatively to common panel-weightings.