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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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    40. repec:taf:jnlbes:v:30:y:2012:i:2:p:165-172 is not listed on IDEAS
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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).

    More about this item

    Keywords

    Fuel pricing behavior; asymmetry; daily data; cross-sectional aggregation;

    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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