An integrated model for crude oil forecasting: Causality assessment and technical efficiency
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DOI: 10.1016/j.eneco.2022.106467
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Citations
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Cited by:
- Guo, Lili & Huang, Xinya & Li, Yanjiao & Li, Houjian, 2023. "Forecasting crude oil futures price using machine learning methods: Evidence from China," Energy Economics, Elsevier, vol. 127(PA).
- Li, Ye & Chen, Yiyan & Lean, Hooi Hooi, 2024. "Geopolitical risk and crude oil price predictability: Novel decomposition ensemble approach based ternary interval number series," Resources Policy, Elsevier, vol. 92(C).
- Hille, Erik, 2023. "Europe's energy crisis: Are geopolitical risks in source countries of fossil fuels accelerating the transition to renewable energy?," Energy Economics, Elsevier, vol. 127(PA).
- Mohsin, Muhammad & Jamaani, Fouad, 2023. "A novel deep-learning technique for forecasting oil price volatility using historical prices of five precious metals in context of green financing – A comparison of deep learning, machine learning, an," Resources Policy, Elsevier, vol. 86(PA).
- Mohsin, Muhammad & Jamaani, Fouad, 2023. "Green finance and the socio-politico-economic factors’ impact on the future oil prices: Evidence from machine learning," Resources Policy, Elsevier, vol. 85(PA).
- Lahmiri, Salim, 2024. "Fossil energy market price prediction by using machine learning with optimal hyper-parameters: A comparative study," Resources Policy, Elsevier, vol. 92(C).
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Keywords
Oil Price forecasting; Granger causality test; Stochastic frontier analysis; Technical efficiency; Causality assessment;All these keywords.
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