Forecasting stability and growth pact compliance using machine learning

The 2011 reform of the Stability and Growth Pact (1996) strengthened the European Commission's monitoring of EU member states' public finance. Failure to comply with the 3% limit on public deficit triggers an audit. In this paper, we present a machine learning based forecasting model for compliance with the 3% limit. We use data from 2006 to 2018 (a turbulent period including the Global Financial Crisis and the Sovereign Debt Crisis) for the 28 EU member states. After identifying 8 features as predictors among 138 variables, forecasting is performed using a support vector machine (SVM) algorithm. The proposed model achieved a forecasting accuracy of nearly 92% and outperformed the logit model used as a benchmark.

• Publication year

  2023

• Venue

  World Economics

• Publication date

  2023-10-26

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1111/twec.13518
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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