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Assessing the Scoreboard of the EU Macroeconomic Imbalances Procedure: (Machine) Learning from Decisions

Author

Listed:
  • João Amador
  • Tiago Alves

Abstract

This paper uses machine learning methods to identify the macroeconomic variables that are most relevant for the classification of countries along the categories of the EU Macroeconomic Imbalances Procedure (MIP). The random forest algorithm considers the 14 headline indicators of the MIP scoreboard and the set of past decisions taken by the European Commission when classifying countries along the macroeconomic imbalances categories. The algorithm identifies the current account balance, the net international investment position and the unemployment rate as key variables, mostly to classify countries that need corrective action, notably through economic adjustment programmes.

Suggested Citation

  • João Amador & Tiago Alves, 2020. "Assessing the Scoreboard of the EU Macroeconomic Imbalances Procedure: (Machine) Learning from Decisions," Working Papers w202016, Banco de Portugal, Economics and Research Department.
    • Handle: RePEc:ptu:wpaper:w202016
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    File URL: https://www.bportugal.pt/sites/default/files/anexos/papers/wp202016.pdf
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    Cited by:

    1. Fotios Gkatzoglou & Emmanouil Sofianos & Amélie Barbier-Gauchard, 2025. "The EU Public Debt Synchronization: A Complex Networks Approach," Economies, MDPI, vol. 13(7), pages 1-23, June.
    2. João Amador & Paulo Barbosa & João Cortes, 2024. "Distance to Export: A Machine Learning Approach with Portuguese Firms," Working Papers w202420, Banco de Portugal, Economics and Research Department.

    More about this item

    JEL classification:

    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General
    • F15 - International Economics - - Trade - - - Economic Integration

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