Research Discussion Papers, Bank of Finland
Markus Holopainen and Peter Sarlin
Toward robust early-warning models: A horse race, ensembles and model uncertainty
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.
Keywords: financial stability; early-warning models; horse race; ensembles; model uncertainty; (follow links to similar papers)
JEL-Codes: C43; E44; F30; G01; G15; (follow links to similar papers)
34 pages, March 4, 2015
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