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Assessing the scoreboard of the EU macroeconomic imbalances procedure: (machine) learning from decisions

Author

Listed:
  • Tiago Alves

    (ISCTE-IUL)

  • João Amador

    (Banco de Portugal and Nova School of Business and Economics)

  • Francisco Gonçalves

    (Nova School of Business and Economics)

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 MIP categories. The algorithm identifies the unemployment rate, the current account balance, the private sector debt and the net international investment position as key variables in the classification process. We explain how high vs low values for these variables contribute to classifying countries inside or outside each MIP category.

Suggested Citation

  • Tiago Alves & João Amador & Francisco Gonçalves, 2022. "Assessing the scoreboard of the EU macroeconomic imbalances procedure: (machine) learning from decisions," Economics Bulletin, AccessEcon, vol. 42(4), pages 2257-2266.
    • Handle: RePEc:ebl:ecbull:eb-21-00584
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    File URL: http://www.accessecon.com/Pubs/EB/2022/Volume42/EB-22-V42-I4-P186.pdf
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    More about this item

    Keywords

    European Union; Economic integration; Machine learning; Random forests;
    All these keywords.

    JEL classification:

    • F1 - International Economics - - Trade
    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics

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