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Asymmetric fuel price responses under heterogeneity

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  • Balaguer, Jacint
  • Ripollés, Jordi

Abstract

We explore the effect of cross-sectional aggregation of data on estimation and test of asymmetric retail fuel price responses to wholesale price shocks. The analysis is performed on data collected daily from individual fuel stations in the Spanish metropolitan areas of Madrid and Barcelona. While the standard OLS estimator is applied to an error correction model in the case of the aggregated time series, we use the mean group approaches developed by Pesaran and Smith (1995) and Pesaran (2006) to estimate the short- and long-run micro-relations under heterogeneity. We found remarkable differences between the results of estimations using aggregated and disaggregated data, which are highly robust to both datasets considered. Our findings could help to explain many of the results in the literature on this research topic. On the one hand, they suggest that the typical estimation with aggregated data clearly tends to overestimate the persistence of shocks. On the other hand, we show that aggregation may generate a loss of efficiency in econometric estimates that is sufficiently large to hide the existence of the “rockets and feathers” phenomenon.

Suggested Citation

  • Balaguer, Jacint & Ripollés, Jordi, 2013. "Asymmetric fuel price responses under heterogeneity," MPRA Paper 52481, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:52481
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    Cited by:

    1. Jacint Balaguer & Jordi Ripollés, 2016. "Exploring the life of price responses in fuel markets. Mean group data or mean group estimator?," Working Papers 2016/16, Economics Department, Universitat Jaume I, Castellón (Spain).
    2. Emmanuel Asane-Otoo & C. Dannemann, 2021. "Station heterogeneity and asymmetric gasoline price responses," Working Papers V-436-21, University of Oldenburg, Department of Economics, revised Aug 2021.
    3. Chen, Hao & Sun, Zesheng, 2021. "International crude oil price, regulation and asymmetric response of China's gasoline price," Energy Economics, Elsevier, vol. 94(C).
    4. Palencia-González, Francisco J. & Navío-Marco, Julio & Juberías-Cáceres, Gema, 2020. "Analysis of brand influence in the rockets and feathers effect using disaggregated data," Research in International Business and Finance, Elsevier, vol. 52(C).
    5. Bragoudakis, Zacharias & Degiannakis, Stavros & Filis, George, 2020. "Oil and pump prices: Testing their asymmetric relationship in a robust way," Energy Economics, Elsevier, vol. 88(C).
    6. Ederington, Louis H. & Fernando, Chitru S. & Hoelscher, Seth A. & Lee, Thomas K. & Linn, Scott C., 2019. "A review of the evidence on the relation between crude oil prices and petroleum product prices," Journal of Commodity Markets, Elsevier, vol. 13(C), pages 1-15.
    7. Perdiguero, Jordi & Jiménez, Juan Luis, 2021. "Price coordination in the Spanish oil market: The monday effect," Energy Policy, Elsevier, vol. 149(C).
    8. Deltas, George & Polemis, Michael, 2020. "Estimating retail gasoline price dynamics: The effects of sample characteristics and research design," Energy Economics, Elsevier, vol. 92(C).
    9. Cui, Jian & Yang, Hanfang & Wang, Yifan & Yang, Caili, 2023. "Dynamics of the gas retail market under China's price cap regulation," Energy Policy, Elsevier, vol. 174(C).
    10. Bragoudakis, Zacharias & Degiannakis, Stavros & Filis, George, 2019. "Oil and pump prices: Is there any asymmetry in the Greek oil downstream sector?," MPRA Paper 95407, University Library of Munich, Germany.
    11. Balaguer, Jacint & Ripollés, Jordi, 2020. "Do classes of gas stations contribute differently to fuel prices? Evidence to foster effective competition in Spain," Energy Policy, Elsevier, vol. 139(C).
    12. González, Xulia & Moral, María J., 2019. "Effects of antitrust prosecution on retail fuel prices," International Journal of Industrial Organization, Elsevier, vol. 67(C).

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    More about this item

    Keywords

    Fuel pricing behavior; asymmetry; daily data; cross-sectional aggregation;
    All these keywords.

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • D43 - Microeconomics - - Market Structure, Pricing, and Design - - - Oligopoly and Other Forms of Market Imperfection
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General

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