Forecasting U.S. shale gas monthly production using a hybrid ARIMA and metabolic nonlinear grey model
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DOI: 10.1016/j.energy.2018.07.047
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- Syed Aziz Ur Rehman & Yanpeng Cai & Rizwan Fazal & Gordhan Das Walasai & Nayyar Hussain Mirjat, 2017. "An Integrated Modeling Approach for Forecasting Long-Term Energy Demand in Pakistan," Energies, MDPI, vol. 10(11), pages 1-23, November.
- Lin, Chiun-Sin & Liou, Fen-May & Huang, Chih-Pin, 2011. "Grey forecasting model for CO2 emissions: A Taiwan study," Applied Energy, Elsevier, vol. 88(11), pages 3816-3820.
- Arezki, Rabah & Fetzer, Thiemo & Pisch, Frank, 2017.
"On the comparative advantage of U.S. manufacturing: Evidence from the shale gas revolution,"
Journal of International Economics, Elsevier, vol. 107(C), pages 34-59.
- Arezki, Rabah & Fetzer, Thiemo & Pisch, Frank, 2016. "On the comparative advantage of U.S. manufacturing:evidence from the shale gas revolution," LSE Research Online Documents on Economics 69026, London School of Economics and Political Science, LSE Library.
- Arezki, Rabah & Fetzer, Thiemo & Pisch, Frank, 2017. "On the comparative advantage of U.S. manufacturing: evidence from the shale gas revolution," LSE Research Online Documents on Economics 72022, London School of Economics and Political Science, LSE Library.
- Arezki, Rabah & Fetzer, Thiemo, 2016. "On the Comparative Advantage of U.S. Manufacturing: Evidence from the Shale Gas Revolution," CAGE Online Working Paper Series 259, Competitive Advantage in the Global Economy (CAGE).
- Pisch, Frank & Fetzer, Thiemo & Arezki, Rabah, 2017. "On the comparative advantage of U.S. manufacturing: Evidence from the shale gas revolution," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 135678, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
- Rabah Arezki & Thiemo Fetzer & Frank Pisch, 2016. "On the Comparative Advantage of U.S. Manufacturing: Evidence from the Shale Gas Revolution," OxCarre Working Papers 167, Oxford Centre for the Analysis of Resource Rich Economies, University of Oxford.
- Arezki, Rabah & Fetzer, Thiemo, 2016. "On the comparative advantage of U.S. manufacturing:evidence from the shale gas revolution," LSE Research Online Documents on Economics 66410, London School of Economics and Political Science, LSE Library.
- Arezki, Rabah & Fetzer, Thiemo, 2016. "On the Comparative Advantage of U.S. Manufacturing: Evidence from the Shale Gas Revolution," The Warwick Economics Research Paper Series (TWERPS) 1106, University of Warwick, Department of Economics.
- Rabah Arezki & Thiemo Fetzer & Frank Pisch, 2016. "On the comparative advantage of U.S. manufacturing: evidence from the shale gas revolution," CEP Discussion Papers dp1454, Centre for Economic Performance, LSE.
- Arezki, Rabah & Fetzer, Thiemo, 2016. "On the Comparative Advantage of U.S. Manufacturing: Evidence from the Shale Gas Revolution," Economic Research Papers 269719, University of Warwick - Department of Economics.
- Saussay, Aurélien, 2018.
"Can the US shale revolution be duplicated in continental Europe? An economic analysis of European shale gas resources,"
Energy Economics, Elsevier, vol. 69(C), pages 295-306.
- Aurélien Saussay, 2018. "Can the US shale revolution be duplicated in continental Europe? An economic analysis of European shale gas resources," Post-Print hal-01695092, HAL.
- Aurélien Saussay, 2018. "Can the US shale revolution be duplicated in continental Europe? An economic analysis of European shale gas resources," SciencePo Working papers Main hal-01695092, HAL.
- Wang, Qiang & Li, Rongrong, 2017. "Research status of shale gas: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 715-720.
- Zhao, Huiru & Guo, Sen, 2016. "An optimized grey model for annual power load forecasting," Energy, Elsevier, vol. 107(C), pages 272-286.
- Akay, Diyar & Atak, Mehmet, 2007. "Grey prediction with rolling mechanism for electricity demand forecasting of Turkey," Energy, Elsevier, vol. 32(9), pages 1670-1675.
- Wang, Qiang & Chen, Xi & Jha, Awadhesh N. & Rogers, Howard, 2014. "Natural gas from shale formation – The evolution, evidences and challenges of shale gas revolution in United States," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 1-28.
- Shuyu Li & Xue Yang & Rongrong Li, 2018. "Forecasting China’s Coal Power Installed Capacity: A Comparison of MGM, ARIMA, GM-ARIMA, and NMGM Models," Sustainability, MDPI, vol. 10(2), pages 1-15, February.
- Kumar, Ujjwal & Jain, V.K., 2010. "Time series models (Grey-Markov, Grey Model with rolling mechanism and singular spectrum analysis) to forecast energy consumption in India," Energy, Elsevier, vol. 35(4), pages 1709-1716.
- repec:hal:spmain:info:hdl:2441/3vsrea3gla9r5oaa2cle5jrqfh is not listed on IDEAS
- Shuyu Li & Rongrong Li, 2017. "Comparison of Forecasting Energy Consumption in Shandong, China Using the ARIMA Model, GM Model, and ARIMA-GM Model," Sustainability, MDPI, vol. 9(7), pages 1-19, July.
- Shaikh, Faheemullah & Ji, Qiang & Shaikh, Pervez Hameed & Mirjat, Nayyar Hussain & Uqaili, Muhammad Aslam, 2017. "Forecasting China’s natural gas demand based on optimised nonlinear grey models," Energy, Elsevier, vol. 140(P1), pages 941-951.
- Arora, Siddharth & Taylor, James W., 2018. "Rule-based autoregressive moving average models for forecasting load on special days: A case study for France," European Journal of Operational Research, Elsevier, vol. 266(1), pages 259-268.
- Adrian Paylor, 2017. "The social–economic impact of shale gas extraction: a global perspective," Third World Quarterly, Taylor & Francis Journals, vol. 38(2), pages 340-355, February.
- Harleman, Max & Weber, Jeremy G., 2017. "Natural resource ownership, financial gains, and governance: The case of unconventional gas development in the UK and the US," Energy Policy, Elsevier, vol. 111(C), pages 281-296.
- Zhou, P. & Ang, B.W. & Poh, K.L., 2006. "A trigonometric grey prediction approach to forecasting electricity demand," Energy, Elsevier, vol. 31(14), pages 2839-2847.
- Pao, Hsiao-Tien & Tsai, Chung-Ming, 2011. "Modeling and forecasting the CO2 emissions, energy consumption, and economic growth in Brazil," Energy, Elsevier, vol. 36(5), pages 2450-2458.
- Brown, Stephen P.A., 2017. "Natural gas vs. oil in U.S. transportation: Will prices confer an advantage to natural gas?," Energy Policy, Elsevier, vol. 110(C), pages 210-221.
- Bo Zeng & Meng Zhou & Jun Zhang, 2017. "Forecasting the Energy Consumption of China’s Manufacturing Using a Homologous Grey Prediction Model," Sustainability, MDPI, vol. 9(11), pages 1-16, October.
- Erasmo Cadenas & Wilfrido Rivera & Rafael Campos-Amezcua & Christopher Heard, 2016. "Wind Speed Prediction Using a Univariate ARIMA Model and a Multivariate NARX Model," Energies, MDPI, vol. 9(2), pages 1-15, February.
- Wang, Qiang & Li, Rongrong, 2016. "Natural gas from shale formation: A research profile," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 1-6.
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Keywords
Shale gas; United States; Metabolic nonlinear grey model; ARIMA; Hybrid forecasting model;All these keywords.
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