Toward Robust Early-Warning Models: A Horse Race, Ensembles and Model Uncertainty

34 Pages Posted: 24 Mar 2015

See all articles by Markus Holopainen

Markus Holopainen

RiskLab Finland

Peter Sarlin

Hanken School of Economics; RiskLab Finland

Multiple version iconThere are 3 versions of this paper

Date Written: March 4, 2015


​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

JEL Classification: E44, F30, G01, G15, C43

Suggested Citation

Holopainen, Markus and Sarlin, Peter, Toward Robust Early-Warning Models: A Horse Race, Ensembles and Model Uncertainty (March 4, 2015). Bank of Finland Research Discussion Paper No. 6/2015, Available at SSRN: or

Markus Holopainen

RiskLab Finland ( email )

Turku, 20520

Peter Sarlin (Contact Author)

Hanken School of Economics

PO Box 479
FI-00101 Helsinki

RiskLab Finland ( email )

Turku, 20520


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