Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/111263
Authors: 
Sarlin, Peter
von Schweinitz, Gregor
Year of Publication: 
2015
Series/Report no.: 
IWH Discussion Papers 6/2015
Abstract: 
Early-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.
Subjects: 
early-warning models
loss functions
threshold setting
predictive performance
JEL: 
C35
C53
G01
Document Type: 
Working Paper

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