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

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  • Gheorghe-Alexandru Tarta

Abstract

This paper introduces a novel application of Growth-at-Risk (GaR) as an early warning system (EWS) for predicting recessions. By transforming GaR into a classification model, we assess its ability to signal economic downturns across 11 Central and Eastern European (CEE) economies from 2005 to 2024. We compare GaR's performance with logistic regression across eight forecasting horizons. Our findings indicate that GaR slightly outperforms the logit model when financial conditions are used as the primary predictor. However, when additional factors such as agents' expectations and financial flows are incorporated, the performance gap narrows. Our results suggest that Growth-at-Risk can function as an effective early warning system without significant performance trade-offs, while offering a flexible and theoretically grounded alternative. Policymakers can leverage Growth-at-Risk not only as a tail risk instrument but also as a classifier, enhancing their forecasting capabilities.

Suggested Citation

  • Gheorghe-Alexandru Tarta, 2025. "Assessing the Early Warning Capabilities of GaR: A Probabilistic Approach to Recession Detection in CEE Economies," Prague Economic Papers, Prague University of Economics and Business, vol. 2025(3), pages 304-346.
    • Handle: RePEc:prg:jnlpep:v:2025:y:2025:i:3:id:897:p:304-346
    DOI: 10.18267/j.pep.897
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    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • G01 - Financial Economics - - General - - - Financial Crises

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