Dr. Peter Sarlin

Dr. Peter Sarlin
Aktuelle Position

seit 2/17

Vorstandsvorsitzender und leitender Wissenschaftler


seit 1/15

Research Affiliate

Leibniz-Institut für Wirtschaftsforschung Halle (IWH)

seit 1/14


Hanken School of Economics


RiskLab Finland


  • Analyse systemischen Risikos
  • makroprudenzielle Politik
  • maschinelles Lernen und visuelle Analyse

Peter Sarlin ist seit Januar 2015 Research Affiliate am IWH. Er forscht zu den Themen maschinelles Lernen und künstliche Intelligenz.

Peter Sarlin ist Professor an der Hanken School of Economics, Direktor von SILO.AI und Leiter von RiskLab Finland.

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Dr. Peter Sarlin
Dr. Peter Sarlin
Mitglied - Abteilung Makroökonomik
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Optimizing Policymakers’ Loss Functions in Crisis Prediction: Before, Within or After?

Peter Sarlin Gregor von Schweinitz

in: Macroeconomic Dynamics, im Erscheinen


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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