Forecasting regional long-run energy demand: A functional coefficient panel approach

Yoosoon Chang (), Yongok Choi, Chang Sik Kim, J. Miller and Joon Y. Park

Energy Economics, 2021, vol. 96, issue C

Abstract: 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)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (14)

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Related works:
Working Paper: Forecasting Regional Long-Run Energy Demand: A Functional Coefficient Panel Approach (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:96:y:2021:i:c:s0140988321000220

DOI: 10.1016/j.eneco.2021.105117

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