Dr Peter Sarlin

Dr Peter Sarlin
Current Position

since 2/17

Executive Chairman and Chief Scientist

Silo.AI

since 1/15

Research Affiliate

Halle Institute for Economic Research (IWH) – Member of the Leibniz Association

since 1/14

Professor of Practice

Hanken School of Economics

Director

RiskLab Finland

Research Interests

  • systemic risk analytics
  • macroprudential policy
  • machine learning and visual analytics

Peter Sarlin joined the institute as a Research Affiliate in January 2015. His research focuses on machine learning and artificial intelligence.

Peter Sarlin is Professor of Practice at Hanken School of Economics and director of RiskLab Finland as well as executive chairman and chief scientist of Silo.AI.

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Dr Peter Sarlin
Dr Peter Sarlin
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Publications

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

Peter Sarlin Gregor von Schweinitz

in: Macroeconomic Dynamics, forthcoming

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.

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