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Forecasting Regional Long-Run Energy Demand: A Functional Coefficient Panel Approach

Author

Listed:
  • Yoosoon Chang

    (Department of Economics, Indiana University)

  • Yongok Choi

    (School of Economics, Chung-Ang University)

  • Chang Sik Kim

    (Department of Economics, Sungkyunkwan University)

  • J. Isaac Miller

    (Department of Economics, University of Missouri-Columbia)

  • Joon Y. Park

    (Department of Economics, Indiana University and Sungkyunkwan University)

Abstract

Published in Energy Economics (https://doi.org/10.1016/j.eneco.2021.105117) Previous authors have pointed out that energy consumption changes both over time and nonlinearly with income level. Recent methodological advances using functional coefficients allow panel models to capture these features succinctly. In order to forecast a functional coefficient out-of-sample, we use functional principal components analysis (FPCA), reducing the problem of forecasting a surface to a much easier problem of forecasting a small number of smoothly varying time series. Using a panel of 180 countries with data since 1971, we forecast energy consumption to 2035 for Germany, Italy, the US, Brazil, China, and India.

Suggested Citation

  • Yoosoon Chang & Yongok Choi & Chang Sik Kim & J. Isaac Miller & Joon Y. Park, 2019. "Forecasting Regional Long-Run Energy Demand: A Functional Coefficient Panel Approach," Working Papers 1915, Department of Economics, University of Missouri.
    • Handle: RePEc:umc:wpaper:1915
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    References listed on IDEAS

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    2. Liddle, Brantley, 2023. "Is timing everything? Assessing the evidence on whether energy/electricity demand elasticities are time-varying," Energy Economics, Elsevier, vol. 124(C).
    3. Cao, Xin & Zhang, Zechen & Qian, Yuan & Wen, Zongguo, 2024. "The spatial pattern, driving factors and evolutionary trend of energy cooperation and consumption in the “Belt and Road Initiative” countries," Energy, Elsevier, vol. 306(C).
    4. Grzegorz Ślusarz & Dariusz Twaróg & Barbara Gołębiewska & Marek Cierpiał-Wolan & Jarosław Gołębiewski & Philipp Plutecki, 2023. "The Role of Biogas Potential in Building the Energy Independence of the Three Seas Initiative Countries," Energies, MDPI, vol. 16(3), pages 1-23, January.
    5. Xuejun Li & Minghua Jiang & Deyu Cai & Wenqin Song & Yalu Sun, 2024. "A Hybrid Forecasting Model for Electricity Demand in Sustainable Power Systems Based on Support Vector Machine," Energies, MDPI, vol. 17(17), pages 1-16, September.
    6. Miller, J. Isaac & Nam, Kyungsik, 2022. "Modeling peak electricity demand: A semiparametric approach using weather-driven cross-temperature response functions," Energy Economics, Elsevier, vol. 114(C).
    7. Rao, Congjun & Zhang, Yue & Wen, Jianghui & Xiao, Xinping & Goh, Mark, 2023. "Energy demand forecasting in China: A support vector regression-compositional data second exponential smoothing model," Energy, Elsevier, vol. 263(PC).
    8. Xin Ma & Yubin Cai & Hong Yuan & Yanqiao Deng, 2023. "Partially Linear Component Support Vector Machine for Primary Energy Consumption Forecasting of the Electric Power Sector in the United States," Sustainability, MDPI, vol. 15(9), pages 1-26, April.
    9. Brantley Liddle, 2022. "What Is the Temporal Path of the GDP Elasticity of Energy Consumption in OECD Countries? An Assessment of Previous Findings and New Evidence," Energies, MDPI, vol. 15(10), pages 1-12, May.
    10. Wang, You & Gong, Xu, 2022. "Analyzing the difference evolution of provincial energy consumption in China using the functional data analysis method," Energy Economics, Elsevier, vol. 105(C).
    11. Peng, Ying & Liao, Hua & Wang, Fangzhi & Ye, Huiying, 2025. "Optimal path of China's economic structure and energy demand to carbon neutrality," Energy Economics, Elsevier, vol. 141(C).
    12. repec:bny:wpaper:0127 is not listed on IDEAS
    13. Zhao, Jing & Miller, J. Isaac & Binfield, Julian & Thompson, Wyatt, 2022. "Modeling and Forecasting Agricultural Commodity Support in the Developing Countries," Commissioned Papers 321785, International Agricultural Trade Research Consortium.
    14. Yoosoon Chang & Yongok Choi & Chang Sik Kim & J. Isaac Miller & Joon Y. Park, 2024. "Common Trends and Country Specific Heterogeneities in Long-Run World Energy Consumption," CAMA Working Papers 2024-04, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    15. Bennedsen, Mikkel & Hillebrand, Eric & Jensen, Sebastian, 2023. "A neural network approach to the environmental Kuznets curve," Energy Economics, Elsevier, vol. 126(C).
    16. Kyungsik Nam & Won-Ki Seo, 2025. "Nonlinear Temperature Sensitivity of Residential Electricity Demand: Evidence from a Distributional Regression Approach," Papers 2503.07213, arXiv.org.

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    Keywords

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    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

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