In this work we attempt is to locate and analyze via multivariate analysis techniques, highly correlated covariates (factors) which are interrelated with the Gross Domestic Product and therefore are affecting either on short-term or on long-term its shaping. For the analysis, feature selection techniques and model selection criteria are used. The case study focuses on annual data for Greece for the period 1980-2018.

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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.

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