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Forecasting the real price of oil using online search data

Author

Listed:
  • Dean Fantazzini
  • Nikita Fomichev

Abstract

New models to forecast the real price of oil on the basis of macroeconomic indicators and Google search data are proposed. A large-scale out-of-sample forecasting analysis comparing the different models is performed. It is found that models including both Google data and macroeconomic aggregates statistically outperform the competing models in the short term, while multivariate models including only Google data perform best also for medium and long term forecasts up to 24 months ahead. This finding is confirmed by different robustness checks.

Suggested Citation

  • Dean Fantazzini & Nikita Fomichev, 2014. "Forecasting the real price of oil using online search data," International Journal of Computational Economics and Econometrics, Inderscience Enterprises Ltd, vol. 4(1/2), pages 4-31.
  • Handle: RePEc:ids:ijcome:v:4:y:2014:i:1/2:p:4-31
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    Citations

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    Cited by:

    1. Serhan Cevik, 2020. "Where Should We Go? Internet Searches and Tourist Arrivals," IMF Working Papers 20/22, International Monetary Fund.
    2. Havranek, Tomas & Zeynalov, Ayaz, 2018. "Forecasting Tourist Arrivals with Google Trends and Mixed Frequency Data," EconStor Preprints 187420, ZBW - Leibniz Information Centre for Economics.
    3. Fantazzini, Dean & Toktamysova, Zhamal, 2015. "Forecasting German car sales using Google data and multivariate models," International Journal of Production Economics, Elsevier, vol. 170(PA), pages 97-135.
    4. Dean Fantazzini & Mario Maggi, 2014. "Proposed Coal Power Plants and Coal-To-Liquids Plants: Which Ones Survive and Why?," DEM Working Papers Series 082, University of Pavia, Department of Economics and Management.
    5. Havranek, Tomas & Zeynalov, Ayaz, 2018. "Forecasting Tourist Arrivals: Google Trends Meets Mixed Frequency Data," MPRA Paper 90205, University Library of Munich, Germany.
    6. Drachal, Krzysztof, 2018. "Comparison between Bayesian and information-theoretic model averaging: Fossil fuels prices example," Energy Economics, Elsevier, vol. 74(C), pages 208-251.
    7. Dean Fantazzini, 2014. "Nowcasting and Forecasting the Monthly Food Stamps Data in the US Using Online Search Data," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-27, November.
    8. Böhme, Marcus H. & Gröger, André & Stöhr, Tobias, 2020. "Searching for a better life: Predicting international migration with online search keywords," Journal of Development Economics, Elsevier, vol. 142(C).
    9. Yu, Lean & Zhao, Yaqing & Tang, Ling & Yang, Zebin, 2019. "Online big data-driven oil consumption forecasting with Google trends," International Journal of Forecasting, Elsevier, vol. 35(1), pages 213-223.
    10. Zeynalov, Ayaz, 2014. "Nowcasting Tourist Arrivals to Prague: Google Econometrics," MPRA Paper 60945, University Library of Munich, Germany.
    11. Zeynalov, Ayaz, 2017. "Forecasting Tourist Arrivals in Prague: Google Econometrics," MPRA Paper 83268, University Library of Munich, Germany.
    12. Fantazzini, Dean, 2014. "Editorial for the Special Issue on 'Computational Methods for Russian Economic and Financial Modelling'," MPRA Paper 55430, University Library of Munich, Germany.

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