Sarlin, PeterSchweinitz, Gregor2025-05-282015https://epflicht.bibliothek.uni-halle.de/handle/123456789/2476827504144urn:nbn:de:gbv:3:2-485522333329Early-warning models most commonly optimize signaling thresholds on crisis probabilities. The ex-post 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-ofsample 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. 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.Online-Ressource (PDF-Datei: IV, 28 S., 0,62 MB) : graph. Darst.enghttp://rightsstatements.org/vocab/InC/1.0/330Book