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Modelling Technical Progress: An Application of the Stochastic Trend Model to UK Energy Demand

Author

Listed:
  • Lester C. Hunt

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

  • Guy Judge

    (Department of Economics, University of Portsmouth)

  • Yashushi Ninomiya

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

Abstract

The precise role of technical progress in estimated energy demand functions has not been well researched. Traditionally a deterministic time trend has been used, implicitly assuming technical progress continues at a fixed rate over time. In this paper, the structural time series model is employed allowing for a stochastic time trend and stochastic seasonal dummies. Therefore, technical progress and seasonal variation are treated as unobservable components that evolve over time. The conventional deterministic trend model is a restricted case of the structural time series model and found not to be accepted by the data for a number of energy types. Energy demand functions for a variety of energy types are estimated for the UK using unadjusted quarterly data. It is found that technical progress in energy usage does not always exhibit a deterministic trend pattern as the conventional model assumes. It often fluctuates over time and is likely to be affected by a range of exogenous factors but also by changes in energy prices (and possibly income also).

Suggested Citation

  • 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.
    • Handle: RePEc:sur:seedps:99
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    References listed on IDEAS

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    Cited by:

    1. Ismail Kavaz, 2020. "Estimating the Price and Income Elasticities of Crude Oil Import Demand for Turkey," International Econometric Review (IER), Econometric Research Association, vol. 12(2), pages 98-111, September.
    2. Dilaver, Zafer & Hunt, Lester C, 2011. "Modelling and forecasting Turkish residential electricity demand," Energy Policy, Elsevier, vol. 39(6), pages 3117-3127, June.
    3. Tehreem Fatima & Enjun Xia & Muhammad Ahad, 2019. "Oil demand forecasting for China: a fresh evidence from structural time series analysis," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 21(3), pages 1205-1224, June.
    4. 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.
    5. Hunt, Lester C. & Ninomiya, Yasushi, 2005. "Primary energy demand in Japan: an empirical analysis of long-term trends and future CO2 emissions," Energy Policy, Elsevier, vol. 33(11), pages 1409-1424, July.
    6. Rodrigues, Niágara & Losekann, Luciano & Silveira Filho, Getulio, 2018. "Demand of automotive fuels in Brazil: Underlying energy demand trend and asymmetric price response," Energy Economics, Elsevier, vol. 74(C), pages 644-655.
    7. Dilaver, Zafer & Hunt, Lester C., 2011. "Turkish aggregate electricity demand: An outlook to 2020," Energy, Elsevier, vol. 36(11), pages 6686-6696.
    8. Sa'ad, Suleiman, 2011. "Underlying energy demand trends in South Korean and Indonesian aggregate whole economy and residential sectors," Energy Policy, Elsevier, vol. 39(1), pages 40-46, January.
    9. Ackah, Ishmael, 2015. "On the relationship between energy consumption, productivity and economic growth: Evidence from Algeria, Ghana, Nigeria and South Africa," MPRA Paper 64887, University Library of Munich, Germany.
    10. Dilaver, Zafer & Hunt, Lester C., 2011. "Industrial electricity demand for Turkey: A structural time series analysis," Energy Economics, Elsevier, vol. 33(3), pages 426-436, May.
    11. Dilaver, Zafer & Hunt, Lester C., 2021. "Modelling U.S. gasoline demand: A structural time series analysis with asymmetric price responses," Energy Policy, Elsevier, vol. 156(C).
    12. Mikayilov, Jeyhun I. & Darandary, Abdulelah & Alyamani, Ryan & Hasanov, Fakhri J. & Alatawi, Hatem, 2020. "Regional heterogeneous drivers of electricity demand in Saudi Arabia: Modeling regional residential electricity demand," Energy Policy, Elsevier, vol. 146(C).
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    More about this item

    Keywords

    energy demand; technical progress; stochastic trend model; seasonality;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices

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