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Forecasting retail fuel prices with spatial interdependencies

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  • Clements, Adam
  • Otero, Jesús

Abstract

This paper forecasts station-level retail fuel prices using econometric methods, incorporating spatial interdependencies. Error correction models with cross-sectional dependence outperform autoregressive models with wholesale prices or spatial effects, demonstrating the benefits of spatial interdependencies in terms of improved forecasting performance.

Suggested Citation

  • Clements, Adam & Otero, Jesús, 2025. "Forecasting retail fuel prices with spatial interdependencies," Economics Letters, Elsevier, vol. 247(C).
    • Handle: RePEc:eee:ecolet:v:247:y:2025:i:c:s0165176524006128
    DOI: 10.1016/j.econlet.2024.112128
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    References listed on IDEAS

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    More about this item

    Keywords

    Forecasting; Petrol price; Spatial; Cointegration;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices

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