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

39 Pages Posted: 22 Jan 2015 Last revised: 1 Apr 2016

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: January 21, 2015


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.

Keywords: financial stability, early-warning models, horse race, ensembles, model uncertainty

JEL Classification: E440, F300, G010, G150, C430

Suggested Citation

Holopainen, Markus and Sarlin, Peter, Toward Robust Early-Warning Models: A Horse Race, Ensembles and Model Uncertainty (January 21, 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


Here is the Coronavirus
related research on SSRN

Paper statistics

Abstract Views
PlumX Metrics