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

João Amador () and Tiago Alves

Working Papers from Banco de Portugal, Economics and Research Department

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.

JEL-codes: C40 F15 (search for similar items in EconPapers)
Date: 2020
New Economics Papers: this item is included in nep-big, nep-cmp, nep-eec, nep-fdg and nep-opm
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Journal Article: Assessing the scoreboard of the EU macroeconomic imbalances procedure: (machine) learning from decisions (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:ptu:wpaper:w202016

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