Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/212314
Authors: 
Holopainen, Markus
Sarlin, Peter
Year of Publication: 
2015
Series/Report no.: 
Bank of Finland Research Discussion Papers 6/2015
Abstract: 
This paper presents first steps toward robust early-warning models. We conduct a horse race of conventional statistical methods and more recent machine learning methods. As early-warning models based upon one approach are oftentimes 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 early-warning 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.
JEL: 
E44
F30
G01
G15
C43
Persistent Identifier of the first edition: 
ISBN: 
978-952-323-027-9
Document Type: 
Working Paper

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