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Asymmetric Price Responses and the Underlying Energy Demand Trend: Are they Substitutes or Complements? Evidence from Modelling OECD Aggregate Energy Demand

Author

Listed:
  • Olutomi I Adeyemi

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

  • David C Broadstock

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

  • Mona Chitnis

    () (Surrey Energy Economics Centre (SEEC) and Research Group on Lifestyles Values and Environment (RESOLVE), University of Surrey)

  • Lester C Hunt

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

  • Guy Judge

    () (Department of Economics, University of Portsmouth)

Abstract

A number of energy demand studies have considered the importance of modelling Asymmetric Price Responses (APR), for example, the often-cited work of Gately and Huntington (2002). Griffin and Schulman (2005) questioned the asymmetric approach arguing that this is only capturing energy saving technical progress. Huntington (2006), however, showed that for whole economy aggregate energy and oil demand there is a role statistically for both APR and exogenous energy saving technical change. In a separate strand of the literature the idea of the Underlying Energy Demand Trend (UEDT) has been developed, see for example Hunt et al. (2003a and 2003b) and Dimitropoulos et al. (2005). They argue that it is important, in time series energy demand models, to allow for stochastic trends (or UEDTs) based upon the structural time series/dynamic regression methodology recommended by Harvey (1989, 1997). This paper attempts to bring these strands of the literature together by conducting tests for the UEDT and APR in energy demand models within both a panel context (consistent with the Huntington, 2006 approach) and the structural time series modelling framework. A set of tests across a range of specifications using time-series and panel data are therefore undertaken in order to ascertain whether energy saving technical change (or the more general UEDT) and APR are substitutes for each other when modelling energy demand or whether they are actually picking up different influences and are therefore complements. Using annual whole economy data for 17 OECD countries over the period 1960 – 2004 the results suggest that in general the UEDT and ARP are complementary estimation methodologies when modelling aggregate energy demand. It is argued therefore that energy demand modellers should not assume at the outset that one method is superior to the other. Moreover, wherever possible, a general model (be it in a time series or panel context) that includes a ‘non linear UEDT’ and APR should be initially estimated, and only if accepted by the data should symmetry and/or a more restrictive UEDT be imposed.

Suggested Citation

  • Olutomi I Adeyemi & David C Broadstock & Mona Chitnis & Lester C Hunt & Guy Judge, 2008. "Asymmetric Price Responses and the Underlying Energy Demand Trend: Are they Substitutes or Complements? Evidence from Modelling OECD Aggregate Energy Demand," Surrey Energy Economics Centre (SEEC), School of Economics Discussion Papers (SEEDS) 121, Surrey Energy Economics Centre (SEEC), School of Economics, University of Surrey.
  • Handle: RePEc:sur:seedps:121
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    References listed on IDEAS

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    1. Adeyemi, Olutomi I. & Hunt, Lester C., 2007. "Modelling OECD industrial energy demand: Asymmetric price responses and energy-saving technical change," Energy Economics, Elsevier, vol. 29(4), pages 693-709, July.
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    Cited by:

    1. Frondel, Manuel & Vance, Colin, 2013. "Re-Identifying the Rebound: What About Asymmetry?," EconStor Open Access Articles, ZBW - German National Library of Economics, pages 42-54.
    2. David C Broadstock & Lester C Hunt, 2013. "Tying up loose ends: A note on the impact of omitting MA residuals from panel energy demand models based on the Koyck lag transformation," Surrey Energy Economics Centre (SEEC), School of Economics Discussion Papers (SEEDS) 140, Surrey Energy Economics Centre (SEEC), School of Economics, University of Surrey.
    3. Hunt, Lester C. & Ryan, David L., 2015. "Economic modelling of energy services: Rectifying misspecified energy demand functions," Energy Economics, Elsevier, vol. 50(C), pages 273-285.
    4. Boysen-Hogrefe, Jens, 2013. "Der Einfluss des Erdölpreises auf die Energiesteuerprognose," Kiel Working Papers 1849, Kiel Institute for the World Economy (IfW).
    5. repec:eee:eneeco:v:69:y:2018:i:c:p:379-394 is not listed on IDEAS
    6. A. Talha Yalta, 2013. "Small Sample Bootstrap Inference of Level Relationships in the Presence of Autocorrelated Errors: A Large Scale Simulation Study and an Application in Energy Demand," Working Papers 1301, TOBB University of Economics and Technology, Department of Economics.
    7. Adofo, Yaw Osei & Evans, Joanne & Hunt, Lester Charles, 2013. "How sensitive to time period sampling is the asymmetric price response specification in energy demand modelling?," Energy Economics, Elsevier, vol. 40(C), pages 90-109.
    8. Filippini, Massimo & Hunt, Lester C., 2015. "Measurement of energy efficiency based on economic foundations," Energy Economics, Elsevier, vol. 52(S1), pages 5-16.
    9. Yalta, A. Talha & Yalta, A. Yasemin, 2016. "The dynamics of fuel demand and illegal fuel activity in Turkey," Energy Economics, Elsevier, vol. 54(C), pages 144-158.
    10. Wadud, Zia, 2015. "Imperfect reversibility of air transport demand: Effects of air fare, fuel prices and price transmission," Transportation Research Part A: Policy and Practice, Elsevier, vol. 72(C), pages 16-26.
    11. Tajudeen, Ibrahim A., 2015. "Examining the role of energy efficiency and non-economic factors in energy demand and CO2 emissions in Nigeria: Policy implications," Energy Policy, Elsevier, vol. 86(C), pages 338-350.
    12. Adewuyi, Adeolu O., 2016. "Determinants of import demand for non-renewable energy (petroleum) products: Empirical evidence from Nigeria," Energy Policy, Elsevier, vol. 95(C), pages 73-93.
    13. Wadud, Zia, 2014. "The asymmetric effects of income and fuel price on air transport demand," Transportation Research Part A: Policy and Practice, Elsevier, vol. 65(C), pages 92-102.
    14. A. Talha Yalta, 2016. "Bootstrap Inference of Level Relationships in the Presence of Serially Correlated Errors: A Large Scale Simulation Study and an Application in Energy Demand," Computational Economics, Springer;Society for Computational Economics, vol. 48(2), pages 339-366, August.
    15. Galip Altinay & A. Talha Yalta, 2016. "Estimating the evolution of elasticities of natural gas demand: the case of Istanbul, Turkey," Empirical Economics, Springer, vol. 51(1), pages 201-220, August.
    16. Adeyemi, Olutomi I. & Hunt, Lester C., 2014. "Accounting for asymmetric price responses and underlying energy demand trends in OECD industrial energy demand," Energy Economics, Elsevier, vol. 45(C), pages 435-444.
    17. Salisu, Afees A. & Ayinde, Taofeek O., 2016. "Modeling energy demand: Some emerging issues," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 1470-1480.
    18. Olaniyan, Monisola J. & Evans, Joanne, 2014. "The importance of engaging residential energy customers' hearts and minds," Energy Policy, Elsevier, vol. 69(C), pages 273-284.
    19. Atalla, Tarek N. & Hunt, Lester C., 2016. "Modelling residential electricity demand in the GCC countries," Energy Economics, Elsevier, vol. 59(C), pages 149-158.

    More about this item

    Keywords

    Energy Demand; OECD; Asymmetric Price Responses; Underlying Energy Demand Trend.;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • 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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