Interdependency pattern recognition in econometrics: a penalized regularization antidote

Ntotsis, Kimon, Karagrigoriou, Alex and Artemiou, Andreas ORCID: https://orcid.org/0000-0002-7501-4090 2021. Interdependency pattern recognition in econometrics: a penalized regularization antidote. Econometrics 9 (4) , 44. 10.3390/econometrics9040044

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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 proper interpretation, special diagnostics must be recommended and implemented for identification purposes. Nonetheless, in the areas of econometrics and statistics, 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. This work proposes and investigates 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.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Mathematics
Subjects:	Q Science > QA Mathematics
Additional Information:	This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/)
Publisher:	MDPI
ISSN:	2225-1146
Date of First Compliant Deposit:	8 December 2021
Date of Acceptance:	1 December 2021
Last Modified:	16 May 2023 08:16
URI:	https://orca.cardiff.ac.uk/id/eprint/145865

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