Title: A penalized multicollinearity measure for improved model assessment
Authors: Kimon Ntotsis - University of the Aegean (Greece) [presenting]
Alexandros Karagrigoriou - University of The Aegean (Greece)
Andreas Artemiou - Cardiff University (United Kingdom)
Abstract: When it comes to variable interpretation, multicollinearity is among the biggest issues that must be surmounted, especially in this new era of Big Data Analytics. Since even moderate size multicollinearity can prevent a proper interpretation, special diagnostics must be recommended and implemented for identification purposes. Nonetheless, in the area of Finance and International Business, among other fields, these diagnostics are controversial concerning their ``successfulness''. It has been remarked that they frequently fail to do proper model assessment due to information complexity, resulting in model misspecification. The aim is to propose and investigate a robust and easily interpretable methodology, termed Elastic Information Criterion, capable of capturing multicollinearity rather accurately and effectively and thus providing a proper model assessment. The performance is investigated via simulated and real data.