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

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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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    References listed on IDEAS

    as
    1. Knedlik, Tobias, 2014. "The impact of preferences on early warning systems — The case of the European Commission's Scoreboard," European Journal of Political Economy, Elsevier, vol. 34(C), pages 157-166.
    2. Willi Koll & Andrew Watt, 2022. "The Macroeconomic Imbalance Procedure at the Heart of EU Economic Governance Reform," Intereconomics: Review of European Economic Policy, Springer;ZBW - Leibniz Information Centre for Economics;Centre for European Policy Studies (CEPS), vol. 57(1), pages 56-62, January.
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    JEL classification:

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

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