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The Long-Run Forecasting of Energy Prices Using the Model of Shifting Trend

Author

Listed:
  • Stanislav Radchenko

    (UNC at Charlotte)

Abstract

This paper constructs long-term forecasts of energy prices using a reduced form model of shifting trend developed by Pindyck (1999). A Gibbs sampling algorithm is developed to estimate models with a shifting trend line which are used to construct 10-period-ahead and 15-period ahead forecasts. An advantage of forecasts from this model is that they are not very influenced by the presence of large, long-lived increases and decreases in energy prices. The forecasts form shifting trends model are combined with forecasts from the random walk model and the autoregressive model to substantially decrease the mean forecast squared error compared to each individual model.

Suggested Citation

  • Stanislav Radchenko, 2005. "The Long-Run Forecasting of Energy Prices Using the Model of Shifting Trend," Econometrics 0502002, University Library of Munich, Germany.
    • Handle: RePEc:wpa:wuwpem:0502002
    Note: Type of Document - pdf; pages: 29
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    References listed on IDEAS

    as
    1. Paul Cashin & C. John McCDermott, 2002. "The Long-Run Behavior of Commodity Prices: Small Trends and Big Variability," IMF Staff Papers, Palgrave Macmillan, vol. 49(2), pages 1-2.
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    3. Jean-Thomas Bernard & Lynda Khalaf & Maral Kichian, 2004. "Structural Change and Forecasting Long-Run Energy Prices," Staff Working Papers 04-5, Bank of Canada.
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    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Scarpa, Elisa & Longo, Chiara & Manera, Matteo & Markandya, Anil, 2007. "Evaluating the Empirical Performance of Alternative Econometric Models for Oil Price Forecasting," International Energy Markets Working Papers 12118, Fondazione Eni Enrico Mattei (FEEM).
    2. Bastianin, Andrea & Manera, Matteo & Markandya, Anil & Scarpa, Elisa, 2011. "Oil Price Forecast Evaluation with Flexible Loss Functions," Energy: Resources and Markets 120042, Fondazione Eni Enrico Mattei (FEEM).
    3. Huang, Wenyang & Gao, Tianxiao & Hao, Yun & Wang, Xiuqing, 2023. "Transformer-based forecasting for intraday trading in the Shanghai crude oil market: Analyzing open-high-low-close prices," Energy Economics, Elsevier, vol. 127(PA).
    4. Maslyuk, Svetlana & Smyth, Russell, 2008. "Unit root properties of crude oil spot and futures prices," Energy Policy, Elsevier, vol. 36(7), pages 2591-2600, July.
    5. repec:bla:rdevec:v:14:y:2010:i:s1:p:499-519 is not listed on IDEAS
    6. Zhongbao Zhou & Ke Duan & Ling Lin & Qianying Jin, 2015. "Forecasting long-term and short-term crude oil price: a comparison of the predictive abilities of competing models," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 38(4/5/6), pages 286-297.
    7. Ozdemir, Zeynel Abidin & Gokmenoglu, Korhan & Ekinci, Cagdas, 2013. "Persistence in crude oil spot and futures prices," Energy, Elsevier, vol. 59(C), pages 29-37.
    8. Lang, Korbinian & Auer, Benjamin R., 2020. "The economic and financial properties of crude oil: A review," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    9. Giliola Frey & Matteo Manera & Anil Markandya & Elisa Scarpa, 2009. "Econometric Models for Oil Price Forecasting: A Critical Survey," CESifo Forum, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 10(01), pages 29-44, April.

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    More about this item

    Keywords

    energy forecasting; oil price; coal price; natural gas price; shifting trends model; long term forecasting;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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