Please use this identifier to cite or link to this item: http://hdl.handle.net/10071/32876
Author(s): Alves, T.
Amador, J.
Gonçalves, F.
Date: 2022
Title: Assessing the scoreboard of the EU macroeconomic imbalances procedure: (Machine) learning from decisions
Journal title: Economics Bulletin
Volume: 42
Number: 4
Pages: 2257 - 2266
Reference: Alves, T., Amador, J., & Gonçalves, F. (2022). Assessing the scoreboard of the EU macroeconomic imbalances procedure: (Machine) learning from decisions. Economics Bulletin, 42(4), 2257-2266. http://www.accessecon.com/pubs/eb/default.aspx?topic=Abstract&PaperID=eb-21-00584
ISSN: 1545-2921
Keywords: European Union
Economic integration
Machine learning
Random forests
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.
Peerreviewed: yes
Access type: Open Access
Appears in Collections:DINÂMIA'CET-RI - Artigos em revistas internacionais com arbitragem científica

Files in This Item:
File SizeFormat 
article_104092.pdf1,55 MBAdobe PDFView/Open


FacebookTwitterDeliciousLinkedInDiggGoogle BookmarksMySpaceOrkut
Formato BibTex mendeley Endnote Logotipo do DeGóis Logotipo do Orcid 

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.