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Estimating sectoral demands for electricity using the pooled mean group method

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  • Gautam, Tej K.
  • Paudel, Krishna P.

Abstract

This paper examines the demand for electricity in the residential, commercial, and industrial sectors of the Northeastern United States using state-level panel data over the period from 1997 to 2011. It applies panel unit root and cointegration tests and then estimates the parameters using the pooled mean group method. The panel unit root and cointegration tests show that the series are integrated of order one and cointegrated. The electricity demand for the residential sector is responsive to its own price in the long run, with own price elasticity being −0.11, but irresponsive to own price in the short run. The long run income elasticities of electricity demand for the residential, commercial, and industrial sectors are 0.93, 0.53, and 1.95, respectively. Higher income elasticity implies that energy efficient appliances and the regulation of housing structures might be effective policy tools to promote energy conservation. The short run impact of fuel oil price is significant in the residential and commercial sectors. Cooling degree days have significant positive effects on the demand for electricity in the residential and commercial sectors. The long run cross price elasticities for natural gas in the residential and commercial sectors are 0.095 and 0.105, respectively.

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  • Gautam, Tej K. & Paudel, Krishna P., 2018. "Estimating sectoral demands for electricity using the pooled mean group method," Applied Energy, Elsevier, vol. 231(C), pages 54-67.
  • Handle: RePEc:eee:appene:v:231:y:2018:i:c:p:54-67
    DOI: 10.1016/j.apenergy.2018.09.023
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    2. Andruszkiewicz, Jerzy & Lorenc, Józef & Weychan, Agnieszka, 2020. "Seasonal variability of price elasticity of demand of households using zonal tariffs and its impact on hourly load of the power system," Energy, Elsevier, vol. 196(C).
    3. Javier Bueno & Desiderio Romero-Jordán & Pablo del Río, 2020. "Analysing the Drivers of Electricity Demand in Spain after the Economic Crisis," Energies, MDPI, vol. 13(20), pages 1-18, October.
    4. Tadahiro Nakajima, 2019. "Expectations for Statistical Arbitrage in Energy Futures Markets," JRFM, MDPI, vol. 12(1), pages 1-12, January.
    5. Phuc Canh, Nguyen & Trung Thong, Nguyen, 2020. "Nexus between financialisation and natural resources rents: Empirical evidence in a global sample," Resources Policy, Elsevier, vol. 66(C).
    6. Cao, K.H. & Qi, H.S. & Li, R. & Woo, C.K. & Tishler, A. & Zarnikau, J., 2023. "An experiment in own-price elasticity estimation for non-residential electricity demand in the U.S," Utilities Policy, Elsevier, vol. 81(C).
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    More about this item

    Keywords

    Electricity; Price elasticity; Pooled mean group; Unit root; Cointegration; Sectoral demand;
    All these keywords.

    JEL classification:

    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • L97 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Utilities: General
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities

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