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Published online by Cambridge University Press:  21 February 2019

Peter Sarlin
RiskLab at Arcada and Hanken School of Economics, and Silo.AI
Gregor von Schweinitz*
Halle Institute for Economic Research (IWH) and University of Leipzig
Address correspondence to: Gregor von Schweinitz, Department of Macroeconomics, Halle Institute for Economic Research, Kleine Märkerstr. 8, 06108 Halle (Saale), Germany. e-mail: Phone: +49 345 7753 744.
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Recurring financial instabilities have led policymakers to rely on early-warning models to signal financial vulnerabilities. These models rely on ex-post optimization of signaling thresholds on crisis probabilities accounting for preferences between forecast errors, but come with the crucial drawback of unstable thresholds in recursive estimations. We propose two alternatives for threshold setting with similar or better out-of-sample performance: (i) including preferences in the estimation itself and (ii) setting thresholds ex-ante according to preferences only. Given probabilistic model output, it is intuitive that a decision rule is independent of the data or model specification, as thresholds on probabilities represent a willingness to issue a false alarm vis-à-vis missing a crisis. We provide real-world and simulation evidence that this simplification results in stable thresholds, while keeping or improving on out-of-sample performance. Our solution is not restricted to binary-choice models, but directly transferable to the signaling approach and all probabilistic early-warning models.

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This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (, which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
© Cambridge University Press 2019


Research of Gregor von Schweinitz was partly funded by the European Regional Development Fund through the programme “Investing in your Future” and by the IWH Speed Project 2014/02. Parts of this work have been completed at the Financial Stability Surveillance Division of the ECB DG Macroprudential Policy and Financial Stability. The authors are grateful for the suggestions of two anonymous referees, useful comments from Bernd Amann, Carsten Detken, Makram El-Shagi, Jan-Hannes Lang, Tuomas Peltonen, and Peter Welz, and discussion at the following seminars and conferences: Third HenU-INFER Workshop on Applied Macroeconomics, IWH Economic Research Seminar, Goethe University Brown Bag Seminar, ECB Financial Stability Seminar, Deutsche Bundesbank Early-Warning Modeling Seminar and the 2015 CEUS Workshop. An online appendix to this paper as well as replication material are supplied at


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