Forecasting the Stability and Growth Pact compliance using Machine Learning

Kea Baret (k.baret@unistra.fr), Amelie Barbier-Gauchard (abarbier@unistra.fr) and Theophilos Papadimitriou
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Kea Baret: University of Strasbourg
Amelie Barbier-Gauchard: University of Strasbourg

No 2022.11, Working Papers from International Network for Economic Research - INFER

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.

Keywords: Fiscal Rules; Fiscal Compliance; Stability and Growth Pact; Machine learning. (search for similar items in EconPapers)
JEL-codes: F (search for similar items in EconPapers)
Pages: 34 pages
Date: 2022
New Economics Papers: this item is included in nep-big, nep-cmp, nep-eec and nep-for
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Working Paper: Forecasting the Stability and Growth Pact compliance using Machine Learning (2023)
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