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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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    1. Stephane Dees & Pedro Soares Brinca, 2013. "Consumer confidence as a predictor of consumption spending: Evidence for the United States and the Euro area," International Economics, CEPII research center, issue 134, pages 1-14.
    2. Alquist, Ron & Kilian, Lutz & Vigfusson, Robert J., 2013. "Forecasting the Price of Oil," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 427-507, Elsevier.
    3. Baghestani, Hamid, 1992. "On the Formation of Expected Inflation under Various Conditions: Some Survey Evidence," The Journal of Business, University of Chicago Press, vol. 65(2), pages 281-293, April.
    4. Ryan Kellogg, 2014. "The Effect of Uncertainty on Investment: Evidence from Texas Oil Drilling," American Economic Review, American Economic Association, vol. 104(6), pages 1698-1734, June.
    5. repec:cii:cepiei:2013-q2-134-1 is not listed on IDEAS
    6. Yu, Lean & Wang, Shouyang & Lai, Kin Keung, 2008. "Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm," Energy Economics, Elsevier, vol. 30(5), pages 2623-2635, September.
    7. Hunt Allcott & Nathan Wozny, 2014. "Gasoline Prices, Fuel Economy, and the Energy Paradox," The Review of Economics and Statistics, MIT Press, vol. 96(5), pages 779-795, December.
    8. Meghan R. Busse & Christopher R. Knittel & Florian Zettelmeyer, 2009. "Pain at the Pump: The Differential Effect of Gasoline Prices on New and Used Automobile Markets," NBER Working Papers 15590, National Bureau of Economic Research, Inc.
    9. James D. Hamilton, 2009. "Understanding Crude Oil Prices," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 179-206.
    10. Christiane Baumeister & Lutz Kilian, 2011. "Real-Time Forecasts of the Real Price of Oil," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(2), pages 326-336, September.
    11. Soren T. Anderson & Ryan Kellogg & James M. Sallee & Richard T. Curtin, 2011. "Forecasting Gasoline Prices Using Consumer Surveys," American Economic Review, American Economic Association, vol. 101(3), pages 110-114, May.
    12. Holden, K & Peel, D A, 1990. "On Testing for Unbiasedness and Efficiency of Forecasts," The Manchester School of Economic & Social Studies, University of Manchester, vol. 58(2), pages 120-127, June.
    13. Fair, Ray C & Shiller, Robert J, 1990. "Comparing Information in Forecasts from Econometric Models," American Economic Review, American Economic Association, vol. 80(3), pages 375-389, June.
    14. Tara Sinclair & H. O. Stekler & L. Kitzinger, 2010. "Directional forecasts of GDP and inflation: a joint evaluation with an application to Federal Reserve predictions," Applied Economics, Taylor & Francis Journals, vol. 42(18), pages 2289-2297.
    15. Pinelopi Koujianou Goldberg, 1998. "The Effects of the Corporate Average Fuel Efficiency Standards in the US," Journal of Industrial Economics, Wiley Blackwell, vol. 46(1), pages 1-33, March.
    16. Schnader, M H & Stekler, H O, 1990. "Evaluating Predictions of Change," The Journal of Business, University of Chicago Press, vol. 63(1), pages 99-107, January.
    17. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    18. Robert B. Barsky & Lutz Kilian, 2002. "Do We Really Know That Oil Caused the Great Stagflation? A Monetary Alternative," NBER Chapters, in: NBER Macroeconomics Annual 2001, Volume 16, pages 137-198, National Bureau of Economic Research, Inc.
    19. Gillman, Max & Nakov, Anton, 2009. "Monetary effects on nominal oil prices," The North American Journal of Economics and Finance, Elsevier, vol. 20(3), pages 239-254, December.
    20. James A. Kahn, 1986. "Gasoline Prices and the Used Automobile Market: A Rational Expectations Asset Price Approach," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 101(2), pages 323-339.
    21. Granger, C. W. J. & Newbold, Paul, 1986. "Forecasting Economic Time Series," Elsevier Monographs, Elsevier, edition 2, number 9780122951831 edited by Shell, Karl.
    22. Merton, Robert C, 1981. "On Market Timing and Investment Performance. I. An Equilibrium Theory of Value for Market Forecasts," The Journal of Business, University of Chicago Press, vol. 54(3), pages 363-406, July.
    23. Malcolm P. Baker & E. Scott Mayfield & John E. Parsons, 1998. "Alternative Models of Uncertain Commodity Prices for Use with Modern Asset Pricing Methods," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 115-148.
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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. Baghestani, Hamid, 2019. "An analysis of vehicle-buying attitudes of US consumers," Research in Transportation Economics, Elsevier, vol. 75(C), pages 62-68.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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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