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Forecasting the Stability and Growth Pact compliance using Machine Learning

Author

Listed:
  • Kea Baret

    (University of Strasbourg)

  • Amelie Barbier-Gauchard

    (University of Strasbourg)

  • Theophilos Papadimitriou

    (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 138 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 was used as benchmark.

Suggested Citation

  • Kea Baret & Amelie Barbier-Gauchard & Theophilos Papadimitriou, 2022. "Forecasting the Stability and Growth Pact compliance using Machine Learning," Working Papers 2022.11, International Network for Economic Research - INFER.
    • Handle: RePEc:inf:wpaper:2022.11
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    References listed on IDEAS

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    1. Amélie Barbier-Gauchard & Kea Baret & Alexandru Minea, 2021. "National fiscal rules and fiscal discipline in the European Union," Applied Economics, Taylor & Francis Journals, vol. 53(20), pages 2337-2359, April.
    2. Mr. Anthony M Annett, 2000. "Social Fractionalization, Political Instability, and the Size of Government," IMF Working Papers 2000/082, International Monetary Fund.
    3. European Fiscal Board (EFB), 2020. "Assessment of the fiscal stance appropiate for the euro area in 2021," Reports 2020, European Fiscal Board.
    4. Henryk Bąk & Sebastian Maciejewski, 2017. "The symmetry of demand and supply shocks in the European Monetary Union," Bank i Kredyt, Narodowy Bank Polski, vol. 48(1), pages 1-44.
    5. Susan Athey, 2018. "The Impact of Machine Learning on Economics," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 507-547, National Bureau of Economic Research, Inc.
    6. Bergman, U. Michael & Hutchison, Michael M. & Jensen, Svend E. Hougaard, 2016. "Promoting sustainable public finances in the European Union: The role of fiscal rules and government efficiency," European Journal of Political Economy, Elsevier, vol. 44(C), pages 1-19.
    7. Amelie BARBIER-GAUCHARD & Kea BARET & Alexandru MINEA, 2019. "National Fiscal Rules Adoption and Fiscal Discipline in the European Union," Working Papers of BETA 2019-40, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
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    Cited by:

    1. Kea BARET, 2021. "Fiscal rules’ compliance and Social Welfare," Working Papers of BETA 2021-38, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    2. Carlos Fonseca Marinheiro, 2021. "The Expenditure Benchmark: Complex and Unsuitable for Independent Fiscal Institutions," Comparative Economic Studies, Palgrave Macmillan;Association for Comparative Economic Studies, vol. 63(3), pages 411-431, September.
    3. Kea BARET, 2021. "Fiscal rules’ compliance and Social Welfare," Working Papers of BETA 2021-50, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.

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    More about this item

    Keywords

    Fiscal Rules; Fiscal Compliance; Stability and Growth Pact; Machine learning.;
    All these keywords.

    JEL classification:

    • F - International Economics

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