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Forecasting the Stability and Growth Pact compliance using Machine Learning

Author

Listed:
  • Kea Baret

    (BETA - Bureau d'Économie Théorique et Appliquée - AgroParisTech - UNISTRA - Université de Strasbourg - Université de Haute-Alsace (UHA) - Université de Haute-Alsace (UHA) Mulhouse - Colmar - UL - Université de Lorraine - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)

  • Amélie Barbier-Gauchard

    (BETA - Bureau d'Économie Théorique et Appliquée - AgroParisTech - UNISTRA - Université de Strasbourg - Université de Haute-Alsace (UHA) - Université de Haute-Alsace (UHA) Mulhouse - Colmar - UL - Université de Lorraine - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)

  • Theophilos Papadimitriou

    (DUTH - Democritus University of Thrace)

Abstract

Since the reinforcement of the Stability and Growth Pact (1996), the European Commission closely monitors public finance in the EU members. A failure to comply with the 3% limit rule on the public deficit by a country triggers an audit. In this paper, we present a Machine Learning based forecasting model for the compliance with the 3% limit rule. To do so, we use data spanning the period from 2006 to 2018 (a turbulent period including the Global Financial Crisis and the Sovereign Debt Crisis) for the 28 EU Member States. A set of eight features are identified as predictors from 141 variables through a feature selection procedure. The forecasting is performed using the Support Vector Machines (SVM). The proposed model reached 91.7% forecasting accuracy and outperformed the Logit model that we used as benchmark.

Suggested Citation

  • Kea Baret & Amélie Barbier-Gauchard & Theophilos Papadimitriou, 2023. "Forecasting the Stability and Growth Pact compliance using Machine Learning," Post-Print hal-03121966, HAL.
    • Handle: RePEc:hal:journl:hal-03121966
    DOI: 10.1111/twec.13518
    Note: View the original document on HAL open archive server: https://hal.science/hal-03121966v2
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    References listed on IDEAS

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