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Forecasting the Oil-Gasoline Price Relationship: Should We Care About the Rockets and the Feathers?

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  • Andrea Bastianin
  • Marzio Galeotti
  • Matteo Manera

Abstract

According to the Rockets and Feathers hypothesis (RFH), the transmission mechanism of positive and negative changes in the price of crude oil to the price of gasoline is asymmetric. Although there have been many contributions documenting that downstream prices are more reactive to increases than to decreases in upstream prices, little is known about the forecasting performance of econometric models incorporating asymmetric price transmission from crude oil to gasoline. In this paper we fill this gap by comparing point, sign and probability forecasts from a variety of Asymmetric-ECM (A-ECM) and Threshold Autoregressive ECM (TAR-ECM) specifications against a standard ECM. Forecasts from A-ECM and TAR-ECM subsume the RFH, while the ECM implies symmetric price transmission from crude oil to gasoline. We quantify the forecast accuracy gains due to incorporating the RFH in predictive models for the prices of gasoline and diesel. We show that the RFH is useless for point forecasting, while it can be exploited to produce more accurate sign and probability forecasts. Finally, we highlight that the forecasting performance of the estimated models is time-varying.

Suggested Citation

  • Andrea Bastianin & Marzio Galeotti & Matteo Manera, 2014. "Forecasting the Oil-Gasoline Price Relationship: Should We Care About the Rockets and the Feathers?," IEFE Working Papers 62, IEFE, Center for Research on Energy and Environmental Economics and Policy, Universita' Bocconi, Milano, Italy.
  • Handle: RePEc:bcu:iefewp:iefewp62
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    Cited by:

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    2. Bastianin, Andrea & Manera, Matteo, 2018. "How Does Stock Market Volatility React To Oil Price Shocks?," Macroeconomic Dynamics, Cambridge University Press, vol. 22(3), pages 666-682, April.
    3. Moses Tule & Afees A. Salisu & Charles Chimeke, 2018. "You are what you eat: The role of oil price in Nigeria inflation forecast," Working Papers 040, Centre for Econometric and Allied Research, University of Ibadan.
    4. Christiane Baumeister & Lutz Kilian & Thomas K. Lee, 2017. "Inside the Crystal Ball: New Approaches to Predicting the Gasoline Price at the Pump," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(2), pages 275-295, March.
    5. Ladislav Kristoufek & Karel Janda & David Zilberman, 2015. "Co-movements of Ethanol Related Prices: Evidence from Brazil and the USA," CAMA Working Papers 2015-11, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    6. Liao, Hua & Cai, Jia-Wei & Yang, Dong-Wei & Wei, Yi-Ming, 2016. "Why did the historical energy forecasting succeed or fail? A case study on IEA's projection," Technological Forecasting and Social Change, Elsevier, vol. 107(C), pages 90-96.
    7. Hamid Baghestani & Jorg Bley, 2020. "Do directional predictions of US gasoline prices reveal asymmetries?," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 44(2), pages 348-360, April.
    8. Bumpass, Donald & Douglas, Christopher & Ginn, Vance & Tuttle, M.H., 2019. "Testing for short and long-run asymmetric responses and structural breaks in the retail gasoline supply chain," Energy Economics, Elsevier, vol. 83(C), pages 311-318.
    9. Qin, Quande & Xie, Kangqiang & He, Huangda & Li, Li & Chu, Xianghua & Wei, Yi-Ming & Wu, Teresa, 2019. "An effective and robust decomposition-ensemble energy price forecasting paradigm with local linear prediction," Energy Economics, Elsevier, vol. 83(C), pages 402-414.
    10. Ederington, Louis H. & Fernando, Chitru S. & Hoelscher, Seth A. & Lee, Thomas K. & Linn, Scott C., 2019. "Characteristics of petroleum product prices: A survey," Journal of Commodity Markets, Elsevier, vol. 14(C), pages 1-15.
    11. Kummer-Noormamode, Sabina, 2018. "The Relationship between Public Debt and Economic Growth: Nonlinearity and Country-Specificity," MPRA Paper 98075, University Library of Munich, Germany.
    12. Sam Olofin & Afees A. Salisu, 2017. "Modelling oil price-inflation nexus: The role of asymmetries and structural breaks," Working Papers 020, Centre for Econometric and Allied Research, University of Ibadan.
    13. Salisu, Afees A. & Isah, Kazeem O. & Oyewole, Oluwatomisin J. & Akanni, Lateef O., 2017. "Modelling oil price-inflation nexus: The role of asymmetries," Energy, Elsevier, vol. 125(C), pages 97-106.
    14. Alberto Bagnai & Christian Alexander Mongeau Ospina, 2016. "Price asymmetries in the European gasoline market," a/ Working Papers Series 1602, Italian Association for the Study of Economic Asymmetries, Rome (Italy).

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    More about this item

    Keywords

    Asymmetries; Forecast Evaluation; Gasoline; Crude Oil; Rockets and Feathers;
    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
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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