cover_ecbwp2025.en.jpg
publiziert in: Macroeconomic Dynamics

Optimizing Policymakers' Loss Functions in Crisis Prediction: Before, Within or After?

Early-warning models most commonly optimize signaling thresholds on crisis probabilities. The expost threshold optimization is based upon a loss function accounting for preferences between forecast errors, but comes with two crucial drawbacks: unstable thresholds in recursive estimations and an in-sample overfit at the expense of out-of-sample performance. We propose two alternatives for threshold setting: (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 simulated and real-world evidence that this simplification results in stable thresholds and improves 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.

27. Februar 2017

Autoren Peter Sarlin Gregor von Schweinitz

Empfohlene Publikationen

cover_macroeconomic-dynamics.jpg

Optimizing Policymakers’ Loss Functions in Crisis Prediction: Before, Within or After?

Peter Sarlin Gregor von Schweinitz

in: Macroeconomic Dynamics, im Erscheinen

Abstract

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.

Publikation lesen

Ansprechpartner

Für Wissenschaftler/innen

Für Journalistinnen/en

Stefanie Müller
Stefanie Müller
Leiterin Kommunikation

Für Rückfragen stehe ich Ihnen gerne zur Verfügung.

+49 345 7753-720 Anfrage per E-Mail
Mitglied der Leibniz-Gemeinschaft LogoTotal-Equality-LogoWeltoffen Logo