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This paper presents first steps toward robust models for crisis prediction.We conduct a horse race of conventional statistical methods and more recentmachine learning methods as early-warning models. As individual models are inthe literature most often built in isolation of other methods, the exercise isof high relevance for assessing the relative performance of a wide variety ofmethods. Further, we test various ensemble approaches to aggregating theinformation products of the built models, providing a more robust basis formeasuring country-level vulnerabilities. Finally, we provide approaches toestimating model uncertainty in early-warning exercises, particularly modelperformance uncertainty and model output uncertainty. The approaches putforward in this paper are shown with Europe as a playground. Generally, ourresults show that the conventional statistical approaches are outperformed bymore advanced machine learning methods, such as k-nearest neighbors and neuralnetworks, and particularly by model aggregation approaches through ensemblelearning.

Toward robust early-warning models: A horse race, ensembles and model uncertainty