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Interdependency Pattern Recognition in Econometrics: A Penalized Regularization Antidote

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  • Kimon Ntotsis

    (Department of Statistics and Actuarial-Financial Mathematics, University of the Aegean, 832 00 Karlovasi, Greece)

  • Alex Karagrigoriou

    (Department of Statistics and Actuarial-Financial Mathematics, University of the Aegean, 832 00 Karlovasi, Greece)

  • Andreas Artemiou

    (School of Mathematics, Cardiff University, Cardiff CF10 3AT, UK)

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.

Suggested Citation

  • Kimon Ntotsis & Alex Karagrigoriou & Andreas Artemiou, 2021. "Interdependency Pattern Recognition in Econometrics: A Penalized Regularization Antidote," Econometrics, MDPI, vol. 9(4), pages 1-13, December.
    • Handle: RePEc:gam:jecnmx:v:9:y:2021:i:4:p:44-:d:695793
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    References listed on IDEAS

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