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Electricity demand analysis and forecasting: A panel cointegration approach

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  • El-Shazly, Alaa

Abstract

This article analyzes the demand for electricity and provides out-of-sample forecasting at the sectoral level using a panel cointegration approach. The econometric model permits cross-sectional heterogeneity within a dynamic framework that incorporates information on relevant income and prices of domestic and foreign goods. Both the short-run dynamics and the long-run slope coefficients are allowed to vary across cross-sections. Also, the testing for unit roots and cointegration in panels allows for heterogeneous fixed effects and deterministic trends. Using Egyptian data, it is shown that the empirical model produces reliable ex-post forecasts near the end of the full sample period. These pseudo forecasts are representative of what one would expect if the forecasting relationship is stationary. The long-run parameter estimates are then used to conduct ex-ante forecasting under plausible assumptions for policy making.

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  • El-Shazly, Alaa, 2013. "Electricity demand analysis and forecasting: A panel cointegration approach," Energy Economics, Elsevier, vol. 40(C), pages 251-258.
  • Handle: RePEc:eee:eneeco:v:40:y:2013:i:c:p:251-258
    DOI: 10.1016/j.eneco.2013.07.003
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    5. Duangnate, Kannika & Mjelde, James W., 2017. "Comparison of data-rich and small-scale data time series models generating probabilistic forecasts: An application to U.S. natural gas gross withdrawals," Energy Economics, Elsevier, vol. 65(C), pages 411-423.
    6. Khan, Muhammad Arshad & Abbas, Faisal, 2016. "The dynamics of electricity demand in Pakistan: A panel cointegration analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 1159-1178.
    7. Gang Du & Chuanwang Sun, 2015. "Determinants of Electricity Demand in Nonmetallic Mineral Products Industry: Evidence from a Comparative Study of Japan and China," Sustainability, MDPI, vol. 7(6), pages 1-25, June.
    8. Cialani, Catia & Mortazavi, Reza, 2018. "Household and industrial electricity demand in Europe," Energy Policy, Elsevier, vol. 122(C), pages 592-600.
    9. Agnolucci, Paolo & De Lipsis, Vincenzo & Arvanitopoulos, Theodoros, 2017. "Modelling UK sub-sector industrial energy demand," Energy Economics, Elsevier, vol. 67(C), pages 366-374.
    10. Khan, Muhammad Arshad, 2015. "Modelling and forecasting the demand for natural gas in Pakistan," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 1145-1159.
    11. Varma, Rashmi & Sushil,, 2019. "Bridging the electricity demand and supply gap using dynamic modeling in the Indian context," Energy Policy, Elsevier, vol. 132(C), pages 515-535.
    12. Boqiang Lin & Weisheng Liu, 2017. "Scenario Prediction of Energy Consumption and CO 2 Emissions in China’s Machinery Industry," Sustainability, MDPI, vol. 9(1), pages 1-18, January.
    13. Steinbuks, Jevgenijs, 2019. "Assessing the accuracy of electricity production forecasts in developing countries," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1175-1185.
    14. Schulte, Isabella & Heindl, Peter, 2017. "Price and income elasticities of residential energy demand in Germany," Energy Policy, Elsevier, vol. 102(C), pages 512-528.
    15. Brantley Liddle & Fakhri Hasanov, 2022. "Industry electricity price and output elasticities for high-income and middle-income countries," Empirical Economics, Springer, vol. 62(3), pages 1293-1319, March.
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    More about this item

    Keywords

    Electricity demand analysis; Forecasting; Panel cointegration;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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