A time-frequency-based interval decomposition ensemble method for forecasting gasoil prices under the trend of low-carbon development
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DOI: 10.1016/j.eneco.2024.107609
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- Zimmer, Anne & Koch, Nicolas, 2017. "Fuel consumption dynamics in Europe: Tax reform implications for air pollution and carbon emissions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 106(C), pages 22-50.
- Paulo Rodrigues & Nazarii Salish, 2015. "Modeling and forecasting interval time series with threshold models," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 9(1), pages 41-57, March.
- Karagiannis, Stelios & Panagopoulos, Yannis & Vlamis, Prodromos, 2015. "Are unleaded gasoline and diesel price adjustments symmetric? A comparison of the four largest EU retail fuel markets," Economic Modelling, Elsevier, vol. 48(C), pages 281-291.
- Lin, Wei & González-Rivera, Gloria, 2016.
"Interval-valued time series models: Estimation based on order statistics exploring the Agriculture Marketing Service data,"
Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 694-711.
- Gloria Gonzalez-Rivera & Wei Lin, 2015. "Interval-valued Time Series Models: Estimation based on Order Statistics. Exploring the Agriculture Marketing Service Data," Working Papers 201505, University of California at Riverside, Department of Economics.
- Haywood, Luke & Jakob, Michael, 2023. "The role of the emissions trading scheme 2 in the policy mix to decarbonize road transport in the European Union," Transport Policy, Elsevier, vol. 139(C), pages 99-108.
- Long, Zoe & Kitt, Shelby & Axsen, Jonn, 2021. "Who supports which low-carbon transport policies? Characterizing heterogeneity among Canadian citizens," Energy Policy, Elsevier, vol. 155(C).
- Bernard, Jean-Thomas & Kichian, Maral, 2019. "The long and short run effects of British Columbia's carbon tax on diesel demand," Energy Policy, Elsevier, vol. 131(C), pages 380-389.
- Miao, Hong & Ramchander, Sanjay & Wang, Tianyang & Yang, Dongxiao, 2017. "Influential factors in crude oil price forecasting," Energy Economics, Elsevier, vol. 68(C), pages 77-88.
- Wei Yang & Ai Han & Yongmiao Hong & Shouyang Wang, 2016. "Analysis of crisis impact on crude oil prices: a new approach with interval time series modelling," Quantitative Finance, Taylor & Francis Journals, vol. 16(12), pages 1917-1928, December.
- He, Angela W.W. & Kwok, Jerry T.K. & Wan, Alan T.K., 2010. "An empirical model of daily highs and lows of West Texas Intermediate crude oil prices," Energy Economics, Elsevier, vol. 32(6), pages 1499-1506, November.
- Sun, Yuying & Han, Ai & Hong, Yongmiao & Wang, Shouyang, 2018. "Threshold autoregressive models for interval-valued time series data," Journal of Econometrics, Elsevier, vol. 206(2), pages 414-446.
- Matthew T. Ballew & Jennifer R. Marlon & Matthew H. Goldberg & Edward W. Maibach & Seth A. Rosenthal & Emily Aiken & Anthony Leiserowitz, 2022. "Changing minds about global warming: vicarious experience predicts self-reported opinion change in the USA," Climatic Change, Springer, vol. 173(3), pages 1-25, August.
- Chai, Jian & Xing, Li-Min & Zhou, Xiao-Yang & Zhang, Zhe George & Li, Jie-Xun, 2018. "Forecasting the WTI crude oil price by a hybrid-refined method," Energy Economics, Elsevier, vol. 71(C), pages 114-127.
- Yanan He & Ai Han & Yongmiao Hong & Yuying Sun & Shouyang Wang, 2021. "Forecasting crude oil price intervals and return volatility via autoregressive conditional interval models," Econometric Reviews, Taylor & Francis Journals, vol. 40(6), pages 584-606, July.
- Sun, Yuying & Zhang, Xun & Hong, Yongmiao & Wang, Shouyang, 2019. "Asymmetric pass-through of oil prices to gasoline prices with interval time series modelling," Energy Economics, Elsevier, vol. 78(C), pages 165-173.
- So-Yun Jeong & Jae-Wook Kim & Han-Young Joo & Young-Seo Kim & Joo-Hyun Moon, 2021. "Development and Application of a Big Data Analysis-Based Procedure to Identify Concerns about Renewable Energy," Energies, MDPI, vol. 14(16), pages 1-13, August.
- Javier Arroyo & Rosa Espínola & Carlos Maté, 2011. "Different Approaches to Forecast Interval Time Series: A Comparison in Finance," Computational Economics, Springer;Society for Computational Economics, vol. 37(2), pages 169-191, February.
- Zhao, Yang & Li, Jianping & Yu, Lean, 2017. "A deep learning ensemble approach for crude oil price forecasting," Energy Economics, Elsevier, vol. 66(C), pages 9-16.
- Xiong, Tao & Bao, Yukun & Hu, Zhongyi, 2013. "Beyond one-step-ahead forecasting: Evaluation of alternative multi-step-ahead forecasting models for crude oil prices," Energy Economics, Elsevier, vol. 40(C), pages 405-415.
- Jian Chai & Youhong Zhou & Ting Liang & Limin Xing & Kin Keung Lai, 2016. "Impact of International Oil Price on Energy Conservation and Emission Reduction in China," Sustainability, MDPI, vol. 8(6), pages 1-17, May.
- Xiong, Tao & Li, Chongguang & Bao, Yukun, 2017. "Interval-valued time series forecasting using a novel hybrid HoltI and MSVR model," Economic Modelling, Elsevier, vol. 60(C), pages 11-23.
- Drachal, Krzysztof, 2016. "Forecasting spot oil price in a dynamic model averaging framework — Have the determinants changed over time?," Energy Economics, Elsevier, vol. 60(C), pages 35-46.
- Chang, Kai & Zhang, Chao, 2018. "Asymmetric dependence structure between emissions allowances and wholesale diesel/gasoline prices in emerging China's emissions trading scheme pilots," Energy, Elsevier, vol. 164(C), pages 124-136.
- Li, Xin & Ma, Jian & Wang, Shouyang & Zhang, Xun, 2015. "How does Google search affect trader positions and crude oil prices?," Economic Modelling, Elsevier, vol. 49(C), pages 162-171.
- Wang, Huai-zhi & Li, Gang-qiang & Wang, Gui-bin & Peng, Jian-chun & Jiang, Hui & Liu, Yi-tao, 2017. "Deep learning based ensemble approach for probabilistic wind power forecasting," Applied Energy, Elsevier, vol. 188(C), pages 56-70.
- Erutku, Can, 2019. "Carbon pricing pass-through: Evidence from Ontario and Quebec's wholesale gasoline markets," Energy Policy, Elsevier, vol. 132(C), pages 106-112.
- Valadkhani, Abbas & Smyth, Russell & Vahid, Farshid, 2015. "Asymmetric pricing of diesel at its source," Energy Economics, Elsevier, vol. 52(PA), pages 183-194.
- Wenshuo Dong & Renhua Chen & Xuelin Ba & Suling Zhu, 2023. "Trend Forecasting of Public Concern about Low Carbon Based on Comprehensive Baidu Index and Its Relationship with CO 2 Emissions: The Case of China," Sustainability, MDPI, vol. 15(17), pages 1-23, August.
- Quanying Lu & Yuying Sun & Yongmiao Hong & Shouyang Wang, 2022. "Forecasting interval-valued crude oil prices using asymmetric interval models," Quantitative Finance, Taylor & Francis Journals, vol. 22(11), pages 2047-2061, November.
- Feng Dong & Ruyin Long & Zhengfu Bian & Xihui Xu & Bolin Yu & Ying Wang, 2017. "Applying a Ruggiero three-stage super-efficiency DEA model to gauge regional carbon emission efficiency: evidence from China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 87(3), pages 1453-1468, July.
- Arning, K. & Offermann-van Heek, J. & Ziefle, M., 2021. "What drives public acceptance of sustainable CO2-derived building materials? A conjoint-analysis of eco-benefits vs. health concerns," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
- Yu Qian Ang & Zachary Michael Berzolla & Samuel Letellier-Duchesne & Christoph F. Reinhart, 2023. "Carbon reduction technology pathways for existing buildings in eight cities," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
- Qiao, Kenan & Sun, Yuying & Wang, Shouyang, 2019. "Market inefficiencies associated with pricing oil stocks during shocks," Energy Economics, Elsevier, vol. 81(C), pages 661-671.
- Wang, Yudong & Wu, Chongfeng & Yang, Li, 2016. "Forecasting crude oil market volatility: A Markov switching multifractal volatility approach," International Journal of Forecasting, Elsevier, vol. 32(1), pages 1-9.
- Lu, Quanying & Li, Yuze & Chai, Jian & Wang, Shouyang, 2020. "Crude oil price analysis and forecasting: A perspective of “new triangle”," Energy Economics, Elsevier, vol. 87(C).
- He, Yanan & Wang, Shouyang & Lai, Kin Keung, 2010. "Global economic activity and crude oil prices: A cointegration analysis," Energy Economics, Elsevier, vol. 32(4), pages 868-876, July.
- Sun, Shaolong & Sun, Yuying & Wang, Shouyang & Wei, Yunjie, 2018. "Interval decomposition ensemble approach for crude oil price forecasting," Energy Economics, Elsevier, vol. 76(C), pages 274-287.
- Wang, Minggang & Tian, Lixin & Zhou, Peng, 2018. "A novel approach for oil price forecasting based on data fluctuation network," Energy Economics, Elsevier, vol. 71(C), pages 201-212.
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Keywords
Gasoil price forecasting; TFIDE; Interval prediction; Low-carbon trend;All these keywords.
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