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The presence of a latent factor in gasoline and diesel prices co-movements

Author

Listed:
  • Cosimo Magazzino

    (Roma Tre University)

  • Marco Mele

    (“Niccolò Cusano” University)

  • Claudiu Tiberiu Albulescu

    (Politehnica University of Timisoara)

  • Nicholas Apergis

    (University of Piraeus)

  • Mihai Ioan Mutascu

    (West University of Timisoara
    Zeppelin University
    University of Orleans)

Abstract

This paper proposes a novel approach to identify the presence of a latent factor in the co-movements of gasoline and diesel prices in the three major European Union economies, (France, Germany, and Italy) using daily data from January 3, 2005, to June 28, 2021. More precisely, we advance an artificial neural networks algorithm estimated through a machine learning experiment through the backpropagation system to show that the neural signal is altered by an element that could coincide with a latent factor in the fuel price co-movements. We consider the role of the fuel tax systems and the connection between gasoline and diesel prices in these countries. The estimations indicate the presence of an unobservable component (the latent factor) in the fuel price co-movements, capable of influencing NN. This result validates the previous findings reported in the literature, indicating an excess co-movement in fuel prices. It also has implications in terms of fuel price forecasts in the short run.

Suggested Citation

  • Cosimo Magazzino & Marco Mele & Claudiu Tiberiu Albulescu & Nicholas Apergis & Mihai Ioan Mutascu, 2024. "The presence of a latent factor in gasoline and diesel prices co-movements," Empirical Economics, Springer, vol. 66(5), pages 1921-1939, May.
    • Handle: RePEc:spr:empeco:v:66:y:2024:i:5:d:10.1007_s00181-023-02523-6
    DOI: 10.1007/s00181-023-02523-6
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    References listed on IDEAS

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    More about this item

    Keywords

    Fuel prices co-movement; Artificial neural networks; Latent factor;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices

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