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Predicting gasoline prices using Michigan survey data

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  • Baghestani, Hamid

Abstract

This study investigates the predictive power of Michigan Surveys of Consumers (MSC) data for gasoline prices. Specifically, we utilize the MSC data on both expected inflation and consumer sentiment to construct a vector autoregressive (VAR) model for forecasting gasoline prices for 2003–2014. Our findings indicate that the VAR forecasts are superior to the comparable benchmark forecasts obtained from a univariate integrated moving average (MA) model in terms of both predictive information content and directional accuracy. As such, we conclude that the MSC data on both expected inflation and consumer sentiment have significant predictive information for gasoline prices. Further inspection reveals that the VAR forecasts are particularly accurate for the period since 2008, reinforcing the notion that consumers are “economically” rational.

Suggested Citation

  • Baghestani, Hamid, 2015. "Predicting gasoline prices using Michigan survey data," Energy Economics, Elsevier, vol. 50(C), pages 27-32.
  • Handle: RePEc:eee:eneeco:v:50:y:2015:i:c:p:27-32
    DOI: 10.1016/j.eneco.2015.04.015
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    Citations

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    Cited by:

    1. Baghestani, Hamid, 2019. "An analysis of vehicle-buying attitudes of US consumers," Research in Transportation Economics, Elsevier, vol. 75(C), pages 62-68.
    2. 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.
    3. Hamid Baghestani & Ajalavat Viriyavipart, 2019. "Do factors influencing consumer home-buying attitudes explain output growth?," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 46(5), pages 1104-1115, August.
    4. Hamid Baghestani, 2022. "Mortgage rate predictability and consumer home-buying assessments," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 46(3), pages 593-603, July.
    5. George Deltas & Michael Polemis, 2024. "Price Pass-Through Dependence on the Source of Cost Increases: Evidence from the European Gasoline Market," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 65(2), pages 595-626, September.
    6. Basse, Tobias & Wegener, Christoph, 2022. "Inflation expectations: Australian consumer survey data versus the bond market," Journal of Economic Behavior & Organization, Elsevier, vol. 203(C), pages 416-430.
    7. 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.
    8. Konstandinos Chourmouziadis & Dimitra K. Chourmouziadou & Prodromos D. Chatzoglou, 2021. "Embedding Four Medium-Term Technical Indicators to an Intelligent Stock Trading Fuzzy System for Predicting: A Portfolio Management Approach," Computational Economics, Springer;Society for Computational Economics, vol. 57(4), pages 1183-1216, April.
    9. 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.
    10. Hamid Baghestani & Sehar Fatima, 2021. "Growth in US Durables Spending: Assessing the Impact of Consumer Ability and Willingness to Buy," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 17(1), pages 55-69, April.
    11. Hamid Baghestani, 2017. "Do US consumer survey data help beat the random walk in forecasting mortgage rates?," Cogent Economics & Finance, Taylor & Francis Journals, vol. 5(1), pages 1343017-134, January.

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    More about this item

    Keywords

    Energy prices; Expected inflation; Consumer sentiment; Forecast accuracy;
    All these keywords.

    JEL classification:

    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy
    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles

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