25 Years IWH

cover_ecbwp2025.en.jpg

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. February 2017

Authors Peter Sarlin Gregor von Schweinitz

Whom to contact

For Researchers

For Journalists

Stefanie Müller
Stefanie Müller
Press Officer

If you have any further questions please contact me.

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