Enhancing Model Selection by Obtaining Optimal Tuning Parameters in Elastic-Net Quantile Regression, Application to Crude Oil Prices

Abdullah S. Al-Jawarneh, Ahmed R. M. Alsayed (), Heba N. Ayyoub, Mohd Tahir Ismail, Siok Kun Sek, Kivanç Halil Ariç and Giancarlo Manzi
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Abdullah S. Al-Jawarneh: Department of Mathematics, Faculty of Science, Jerash University, Jerash 26150, Jordan
Ahmed R. M. Alsayed: Department of Economics, Quantitative Methods, and Data Mining Centre, University of Milan, 20122 Milan, Italy
Heba N. Ayyoub: Department of Mathematics, Faculty of Science, Philadelphia University, Amman 19392, Jordan
Mohd Tahir Ismail: School of Mathematical Sciences, Universiti Sains Malaysia, Gelugor 11700, Malaysia
Siok Kun Sek: School of Mathematical Sciences, Universiti Sains Malaysia, Gelugor 11700, Malaysia
Kivanç Halil Ariç: Faculty of Economics and Administrative Sciences, Sivas Cumhuriyet University, 58070 Sivas, Turkey
Giancarlo Manzi: Department of Economics, Quantitative Methods, and Data Mining Centre, University of Milan, 20122 Milan, Italy

JRFM, 2024, vol. 17, issue 8, 1-19

Abstract: Recently, there has been an increased focus on enhancing the accuracy of machine learning techniques. However, there is the possibility to improve it by selecting the optimal tuning parameters, especially when data heterogeneity and multicollinearity exist. Therefore, this study proposed a statistical model to study the importance of changing the crude oil prices in the European Union, in which it should meet state-of-the-art developments on economic, political, environmental, and social challenges. The proposed model is Elastic-net quantile regression, which provides more accurate estimations to tackle multicollinearity, heavy-tailed distributions, heterogeneity, and selecting the most significant variables. The performance has been verified by several statistical criteria. The main findings of numerical simulation and real data application confirm the superiority of the proposed Elastic-net quantile regression at the optimal tuning parameters, as it provided significant information in detecting changes in oil prices. Accordingly, based on the significant selected variables; the exchange rate has the highest influence on oil price changes at high frequencies, followed by retail trade, interest rates, and the consumer price index. The importance of this research is that policymakers take advantage of the vital importance of developing energy policies and decisions in their planning.

Keywords: quantile regression; tuning parameters; penalized regression; multicollinearity; heterogeneity; cross-validation; crude oil price (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2024
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