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Inside the Crystal Ball: New Approaches to Predicting the Gasoline Price at the Pump

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  • Christiane Baumeister
  • Lutz Kilian
  • Thomas K. Lee

Abstract

Appropriate real‐time forecasting models for the US retail price of gasoline yield substantial reductions in the mean‐squared prediction error (MSPE) at horizons up to 2 years as well as substantial increases in directional accuracy. Even greater MSPE reductions are possible by constructing a pooled forecast that assigns equal weight to five of the most successful forecasting models. Pooled forecasts have lower MSPE than the US Energy Information Administration gasoline price forecasts and the gasoline price expectations in the Michigan Survey of Consumers. We also show that as much as 39% of the decline in gas prices between June and December 2014 was predictable. Copyright © 2016 John Wiley & Sons, Ltd.

Suggested Citation

  • 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.
    • Handle: RePEc:wly:japmet:v:32:y:2017:i:2:p:275-295
    DOI: 10.1002/jae.2510
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    Cited by:

    1. John Coglianese & Lucas W. Davis & Lutz Kilian & James H. Stock, 2017. "Anticipation, Tax Avoidance, and the Price Elasticity of Gasoline Demand," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(1), pages 1-15, January.
    2. Sameh Asim Ajlouni & Moh'd Taleb Alodat, 2021. "Gaussian Process Regression for Forecasting Gasoline Prices in Jordan," International Journal of Energy Economics and Policy, Econjournals, vol. 11(3), pages 502-509.
    3. Marco Barassi & Yuqian Zhao, 2018. "Combination Forecasting of Energy Demand in the UK," The Energy Journal, , vol. 39(1_suppl), pages 209-238, June.
    4. Markos Farag, Stephen Snudden, Greg Upton, 2024. "Can Futures Prices Predict the Real Price of Primary Commodities?," LCERPA Working Papers jc0145, Laurier Centre for Economic Research and Policy Analysis, revised 2024.
    5. Christiane Baumeister & Dimitris Korobilis & Thomas K. Lee, 2022. "Energy Markets and Global Economic Conditions," The Review of Economics and Statistics, MIT Press, vol. 104(4), pages 828-844, October.
    6. Christiane Baumeister & Reinhard Ellwanger & Lutz Kilian, 2016. "Did the Renewable Fuel Standard Shift Market Expectations of the Price of Ethanol?," CESifo Working Paper Series 6282, CESifo.
    7. Zouhaier Dhifaoui & Sami Ben Jabeur & Rabeh Khalfaoui & Muhammad Ali Nasir, 2023. "Time‐varying partial‐directed coherence approach to forecast global energy prices with stochastic volatility model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(8), pages 2292-2306, December.
    8. Rangan Gupta & Christian Pierdzioch & Aviral K. Tiwari, 2024. "Gasoline Prices and Presidential Approval Ratings of the United States," Working Papers 202427, University of Pretoria, Department of Economics.
    9. Christiane Baumeister & Lutz Kilian, 2016. "Lower Oil Prices and the U.S. Economy: Is This Time Different?," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 47(2 (Fall)), pages 287-357.
    10. Christiane Baumeister & Lutz Killian, 2016. "Lower Oil Prices and the U.S. Economy: Is This Time Different?," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 47(2 (Fall)), pages 287-357.
    11. Pincheira, Pablo & Jarsun, Nabil, 2020. "Summary of the Paper Entitled: Forecasting Fuel Prices with the Chilean Exchange Rate," MPRA Paper 105056, University Library of Munich, Germany.
    12. Feng Xu & Mohamad Sepehri & Jian Hua & Sergey Ivanov & Julius N. Anyu, 2018. "Time-Series Forecasting Models for Gasoline Prices in China," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 10(12), pages 1-43, December.
    13. Reinhard Ellwanger, Stephen Snudden, 2021. "Predictability of Aggregated Time Series," LCERPA Working Papers bm0127, Laurier Centre for Economic Research and Policy Analysis.
    14. Arunanondchai, Panit & Senia, Mark C. & Capps, Oral, Jr., 2017. "Can U.S. EIA Retail Gasoline Price Forecasts Be Improved Upon?," Reports 285201, Texas A&M University, Agribusiness, Food, and Consumer Economics Research Center.
    15. Gupta, Rangan & Yoon, Seong-Min, 2018. "OPEC news and predictability of oil futures returns and volatility: Evidence from a nonparametric causality-in-quantiles approach," The North American Journal of Economics and Finance, Elsevier, vol. 45(C), pages 206-214.
    16. Pincheira-Brown, Pablo & Bentancor, Andrea & Hardy, Nicolás & Jarsun, Nabil, 2022. "Forecasting fuel prices with the Chilean exchange rate: Going beyond the commodity currency hypothesis," Energy Economics, Elsevier, vol. 106(C).
    17. Ellwanger, Reinhard & Snudden, Stephen, 2023. "Forecasts of the real price of oil revisited: Do they beat the random walk?," Journal of Banking & Finance, Elsevier, vol. 154(C).
    18. Binder, Carola Conces, 2018. "Inflation expectations and the price at the pump," Journal of Macroeconomics, Elsevier, vol. 58(C), pages 1-18.
    19. 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.
    20. Reinhard Ellwanger, Stephen Snudden, Lenin Arango-Castillo, 2023. "Seize the Last Day: Period-End-Point Sampling for Forecasts of Temporally Aggregated Data," LCERPA Working Papers bm0142, Laurier Centre for Economic Research and Policy Analysis.

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    JEL classification:

    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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