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

Yoosoon Chang (), Yongok Choi, Chang Sik Kim, J. Miller and Joon Park
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Joon Park: Department of Economics, Indiana University and Sungkyunkwan University

No 1915, Working Papers from Department of Economics, University of Missouri

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.

Keywords: functional coefficient panel model; functional principal component analysis; energy consumption (search for similar items in EconPapers)
JEL-codes: C14 C23 C51 Q43 (search for similar items in EconPapers)
Pages: 26 pages
Date: 2019-11-25
New Economics Papers: this item is included in nep-ene, nep-for and nep-ore
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Journal Article: Forecasting regional long-run energy demand: A functional coefficient panel approach (2021)
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