Forecasting the Stability and Growth Pact compliance using Machine Learning

Kéa Baret, Amélie Barbier-Gauchard and Theophilos Papadimitriou

Working Papers of BETA from Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg

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.

Keywords: Fiscal Rules; Fiscal Compliance; Stability and Growth Pact; Machine learning. (search for similar items in EconPapers)
JEL-codes: E62 H11 H60 H68 (search for similar items in EconPapers)
Date: 2021
New Economics Papers: this item is included in nep-big, nep-cmp, nep-eec, nep-for and nep-mac
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Related works:
Working Paper: Forecasting the Stability and Growth Pact compliance using Machine Learning (2023)
Working Paper: Forecasting the Stability and Growth Pact compliance using Machine Learning (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:ulp:sbbeta:2021-01

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