 The use of classification models to identify factors differentiating the competitiveness of the EU-15 and EU-13 countriesAgnieszka Kleszcz International Journal of Computational Economics and Econometrics, 2023, vol. 13, issue 1, 110-128 Abstract: This paper reports on a study of the Global Competitiveness Index pillars, aiming to differentiate the European Union countries grouped by their accession year in terms of their competitiveness. A linear (regularised logistic regression) and nonlinear (random forests) classifiers are proposed, to model the relationship between multidimensional economic condition indicators and the country's group. The key discriminators of the competitiveness of the EU-15 (accession before 2004) and the EU-13 (accession in or after 2004) are obtained by analysis of feature importance in classification models. Upon study of 12 competitive indicators from the World Economic Reports (2007-2017 edition) we conclude that the highest disparities between the groups of countries can be observed in infrastructure. Innovation, market size and institutions are the next three most important differentiating factors. A major methodological contribution of the paper is the use of explainable statistical models for identifying key features differentiating groups of countries. Keywords: logistic regression; random forest; European Union; Global Competitiveness Index; GCI; feature importance. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:ijcome:v:13:y:2023:i:1:p:110-128 Access Statistics for this article More articles in International Journal of Computational Economics and Econometrics from Inderscience Enterprises Ltd |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.