Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/162681
Authors: 
Sarlin, Peter
von Schweinitz, Gregor
Year of Publication: 
2017
Series/Report no.: 
ECB Working Paper 2025
Abstract: 
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.
Subjects: 
Early-warning models
Loss functions
Threshold setting
Predictive performance
JEL: 
C35
C53
G01
Persistent Identifier of the first edition: 
ISBN: 
978-92-899-2747-5
Document Type: 
Working Paper

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.