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Modelling and Forecasting Turkish Residential Electricity Demand

Author

Listed:
  • Zafer Dilaver

    (Surrey Energy Economics Centre (SEEC), Department of Economics, University of Surrey)

  • Lester C Hunt

    (Surrey Energy Economics Centre (SEEC), Department of Economics, University of Surrey)

Abstract

This research investigates the relationship between Turkish residential electricity consumption, household total final consumption expenditure and residential electricity prices by applying the structural time series model to annual data over the period 1960 to 2008. Household total final consumption expenditure, real energy prices and an underlying energy demand trend are found to be important drivers of residential electricity demand with the estimated short run and the long run total final consumption expenditure elasticities being 0.38 and 1.57 respectively and the estimated short run and long run price elasticities being -0.09 and -0.38 respectively. Moreover, the estimated underlying energy demand trend, (which, as far as is known, has not been investigated before for the Turkish residential sector) should be of some benefit to Turkish decision makers in terms of energy planning. It provides information about the impact of the implementation of past policies, the influence of technical progress, the changes in consumer behaviour and the effects of energy market structure. Furthermore, based on the estimated equation, and different forecast assumptions, it is predicted that Turkish residential electricity consumption will be somewhere between 48 and 80 TWh by 2020 compared to 40 TWh in 2008.

Suggested Citation

  • Zafer Dilaver & Lester C Hunt, 2010. "Modelling and Forecasting Turkish Residential Electricity Demand," Surrey Energy Economics Centre (SEEC), School of Economics Discussion Papers (SEEDS) 131, Surrey Energy Economics Centre (SEEC), School of Economics, University of Surrey.
  • Handle: RePEc:sur:seedps:131
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    File URL: https://repec.som.surrey.ac.uk/seeds/SEEDS131.pdf
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    References listed on IDEAS

    as
    1. Hunt, Lester C. & Judge, Guy & Ninomiya, Yasushi, 2003. "Underlying trends and seasonality in UK energy demand: a sectoral analysis," Energy Economics, Elsevier, vol. 25(1), pages 93-118, January.
    2. Lester C. Hunt & Guy Judge & Yasushi Ninomiya, 2003. "Modelling underlying energy demand trends," Chapters, in: Lester C. Hunt (ed.), Energy in a Competitive Market, chapter 9, Edward Elgar Publishing.
    3. B. Bhaskara Rao, 2010. "Deterministic and stochastic trends in the time series models: a guide for the applied economist," Applied Economics, Taylor & Francis Journals, vol. 42(17), pages 2193-2202.
    4. World Bank, 2009. "World Development Indicators 2009," World Bank Publications - Books, The World Bank Group, number 4367, December.
    5. John Dimitropoulos & Lester Hunt & Guy Judge, 2005. "Estimating underlying energy demand trends using UK annual data," Applied Economics Letters, Taylor & Francis Journals, vol. 12(4), pages 239-244.
    6. Amarawickrama, Himanshu A. & Hunt, Lester C., 2008. "Electricity demand for Sri Lanka: A time series analysis," Energy, Elsevier, vol. 33(5), pages 724-739.
    7. Hamzacebi, Coskun, 2007. "Forecasting of Turkey's net electricity energy consumption on sectoral bases," Energy Policy, Elsevier, vol. 35(3), pages 2009-2016, March.
    8. Lester C. Hunt & Yasushi Ninomiya, 2003. "Unravelling Trends and Seasonality: A Structural Time Series Analysis of Transport Oil Demand in the UK and Japan," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 63-96.
    9. Lester C. Hunt (ed.), 2003. "Energy in a Competitive Market," Books, Edward Elgar Publishing, number 2519.
    10. Harvey, A C, et al, 1986. "Stochastic Trends in Dynamic Regression Models: An Application to the Employment-Output Equations," Economic Journal, Royal Economic Society, vol. 96(384), pages 975-985, December.
    11. Halicioglu, Ferda, 2007. "Residential electricity demand dynamics in Turkey," Energy Economics, Elsevier, vol. 29(2), pages 199-210, March.
    12. Lester C. Hunt & Guy Judge & Yashushi Ninomiya, 2000. "Modelling Technical Progress: An Application of the Stochastic Trend Model to UK Energy Demand," Surrey Energy Economics Centre (SEEC), School of Economics Discussion Papers (SEEDS) 99, Surrey Energy Economics Centre (SEEC), School of Economics, University of Surrey.
    13. Harvey, Andrew & Scott, Andrew, 1994. "Seasonality in Dynamic Regression Models," Economic Journal, Royal Economic Society, vol. 104(427), pages 1324-1345, November.
    14. Harvey, Andrew C & Koopman, Siem Jan, 1992. "Diagnostic Checking of Unobserved-Components Time Series Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(4), pages 377-389, October.
    15. Harvey, Andrew, 1997. "Trends, Cycles and Autoregressions," Economic Journal, Royal Economic Society, vol. 107(440), pages 192-201, January.
    16. Joao Tovar Jalles, 2009. "Structural time series models and the Kalman filter: a concise review," Nova SBE Working Paper Series wp541, Universidade Nova de Lisboa, Nova School of Business and Economics.
    17. World Bank, 2010. "World Development Indicators 2010," World Bank Publications - Books, The World Bank Group, number 4373, December.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Turkish Residential Electricity Demand; Structural Time Series Model (STSM); Future Scenarios; Energy Demand Modelling and Forecasting.;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • 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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