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Assessing the Early Warning Capabilities of GaR: A Probabilistic Approach to Recession Detection in CEE Economies

Gheorghe-Alexandru Tarta

Prague Economic Papers2025https://doi.org/10.18267/j.pep.897article
ABDC C
Weight
0.50

What the paper says

This paper introduces anovel application ofGrowth-at-Risk (GaR) asanearly warning system (EWS) for predicting recessions. By transforming GaR into aclassification model, we assess its ability tosignal economic downturns across 11 Central and Eastern European (CEE) economies from 2005 to2024. We compare GaR’s performance with logistic regression across eight forecasting horizons. Our findings indicate that GaR slightly outperforms thelogit model when financial conditions are used astheprimary predictor. However, when additional factors such asagents’ expectations and financial flows are incorporated, theperformance gap narrows. Our results suggest that Growth-at-Risk can function asaneffective early warning system without significant performance trade-offs, while offering aflexible and theoretically grounded alternative. Policymakers can leverage Growth-at-Risk not only asatail risk instrument but also asaclassifier, enhancing their forecasting capabilities.

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https://doi.org/https://doi.org/10.18267/j.pep.897

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@article{gheorghe-alexandru2025,
  title        = {{Assessing the Early Warning Capabilities of GaR: A Probabilistic Approach to Recession Detection in CEE Economies}},
  author       = {Gheorghe-Alexandru Tarta},
  journal      = {Prague Economic Papers},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.18267/j.pep.897},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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