Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/154333
Authors: 
Holopainen, Markus
Sarlin, Peter
Year of Publication: 
2016
Series/Report no.: 
ECB Working Paper 1900
Abstract: 
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.
Subjects: 
early-warning models
ensembles
financial stability
horse race
model uncertainty
JEL: 
E44
F30
G01
G15
C43
Persistent Identifier of the first edition: 
ISBN: 
978-92-899-2043-8
Document Type: 
Working Paper

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