Optimizing Policymakers' Loss Functions in Crisis Prediction: Before, Within or After?

31 Pages Posted: 24 Feb 2017

See all articles by Peter Sarlin

Peter Sarlin

Hanken School of Economics; RiskLab Finland

Gregor von Schweinitz

Halle Institute for Economic Research

Date Written: February 23, 2017


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.

Keywords: early-warning models, loss functions, threshold setting, predictive performance

JEL Classification: C35, C53, G01

Suggested Citation

Sarlin, Peter and von Schweinitz, Gregor, Optimizing Policymakers' Loss Functions in Crisis Prediction: Before, Within or After? (February 23, 2017). ECB Working Paper No. 2025, Available at SSRN: https://ssrn.com/abstract=2923174

Peter Sarlin (Contact Author)

Hanken School of Economics

PO Box 479
FI-00101 Helsinki

RiskLab Finland ( email )

Turku, 20520

HOME PAGE: http://risklab.fi/people/peter/

Gregor Von Schweinitz

Halle Institute for Economic Research ( email )

P.O. Box 11 03 61
Kleine Maerkerstrasse 8
D-06017 Halle, 06108

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