Predicting the changes in the WTI crude oil price dynamics using machine learning models
Hasraddin Guliyev and
Resources Policy, 2022, vol. 77, issue C
This study aims to use a monthly dataset from 1991 to 2021 to predict West Texas Intermediate (WTI) oil price dynamics using U.S. macroeconomic and financial factors, as well as a global crisis and crashes. We used advanced machine learning models such as Logistic Regression, Decision Tree, Random Forest, AdaBoost, and XgBoost in this study. According to the results, the XgBoost and Random Forest models outperform traditional models. We also used DeLong statistical test procedures to accurately compare machine learning models' performance. In addition, the study used SHAP - SHapley Additive exPlanations values to support model evaluation and interpretability. This new outline highlights the critical features of the WTI crude oil price prediction and provides appropriate model explanations by utilizing the practical SHAP values. The empirical findings showed that machine learning models could successfully and accurately predict the trend of WTI crude oil price changes. Our findings are important for policymakers, companies, and investors, as well as long-term energy-based economic development.
Keywords: Energy economics; Machine learning models; Crude oil price (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jrpoli:v:77:y:2022:i:c:s030142072200112x
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