  This paper presents first steps toward robust models for crisis prediction.
We conduct a horse race of conventional statistical methods and more recent
machine learning methods as early-warning models. As individual models are in
the literature most often built in isolation of other methods, the exercise is
of high relevance for assessing the relative performance of a wide variety of
methods. Further, we test various ensemble approaches to aggregating the
information products of the built models, providing a more robust basis for
measuring country-level vulnerabilities. Finally, we provide approaches to
estimating model uncertainty in early-warning exercises, particularly model
performance uncertainty and model output uncertainty. The approaches put
forward in this paper are shown with Europe as a playground. Generally, our
results show that the conventional statistical approaches are outperformed by
more advanced machine learning methods, such as k-nearest neighbors and neural
networks, and particularly by model aggregation approaches through ensemble
learning.
