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A Bayesian heterogeneous coefficients spatial autoregressive panel data model of retail fuel duopoly pricing

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  • LeSage, James P.
  • Vance, Colin
  • Chih, Yao-Yu

Abstract

We apply a heterogenous coefficient spatial autoregressive panel model to explore competition/cooperation by duopoly pairs of German fueling stations in setting prices for diesel and e5 fuel. We rely on a Markov Chain Monte Carlo (MCMC) estimation methodology applied with non-informative priors, which produces estimates equivalent to those from (quasi-) maximum likelihood. We explore station-level pricing behavior using pairs of proximately situated fueling stations with no nearby neighbors. Our sample data represents average daily diesel and e5 fuel prices, and refinery cost information covering more than 487 days.

Suggested Citation

  • LeSage, James P. & Vance, Colin & Chih, Yao-Yu, 2017. "A Bayesian heterogeneous coefficients spatial autoregressive panel data model of retail fuel duopoly pricing," Regional Science and Urban Economics, Elsevier, vol. 62(C), pages 46-55.
  • Handle: RePEc:eee:regeco:v:62:y:2017:i:c:p:46-55
    DOI: 10.1016/j.regsciurbeco.2016.11.003
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    Cited by:

    1. Xu, Yuhong & Yang, Zhenlin, 2020. "Specification Tests for Temporal Heterogeneity in Spatial Panel Data Models with Fixed Effects," Regional Science and Urban Economics, Elsevier, vol. 81(C).
    2. Frondel, Manuel & Horvath, Marco & Vance, Colin & Kihm, Alexander, 2019. "Increased market transparency in Germany's gasoline market: What about rockets and feathers?," Ruhr Economic Papers 810, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    3. LeSage, James P. & Chih, Yao-Yu & Vance, Colin, 2019. "Markov Chain Monte Carlo estimation of spatial dynamic panel models for large samples," Computational Statistics & Data Analysis, Elsevier, vol. 138(C), pages 107-125.
    4. Funashima, Yoshito & Ohtsuka, Yoshihiro, 2019. "Spatial crowding-out and crowding-in effects of government spending on the private sector in Japan," Regional Science and Urban Economics, Elsevier, vol. 75(C), pages 35-48.
    5. Horvath, Marco, 2019. "Germany's market transparency unit for fuels: Fostering collusion or competition?," Ruhr Economic Papers 836, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    6. Sommer, Stephan & Vance, Colin, 2021. "Do more chargers mean more electric cars?," Ruhr Economic Papers 893, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    7. Thomas Suesse, 2018. "Estimation of spatial autoregressive models with measurement error for large data sets," Computational Statistics, Springer, vol. 33(4), pages 1627-1648, December.
    8. Elhorst, Paul & Faems, Dries, 2021. "Evaluating proposals in innovation contests: Exploring negative scoring spillovers in the absence of a strict evaluation sequence," Research Policy, Elsevier, vol. 50(4).
    9. Bergantino, Angela S. & Capozza, Claudia & Intini, Mario, 2020. "Empirical investigation of retail fuel pricing: The impact of spatial interaction, competition and territorial factors," Energy Economics, Elsevier, vol. 90(C).
    10. Cornwall, Gary J. & Parent, Olivier, 2017. "Embracing heterogeneity: the spatial autoregressive mixture model," Regional Science and Urban Economics, Elsevier, vol. 64(C), pages 148-161.
    11. J. Paul Elhorst, 2022. "The dynamic general nesting spatial econometric model for spatial panels with common factors: Further raising the bar," Review of Regional Research: Jahrbuch für Regionalwissenschaft, Springer;Gesellschaft für Regionalforschung (GfR), vol. 42(3), pages 249-267, December.

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    More about this item

    Keywords

    Spatial panel data models; Markov Chain Monte Carlo; Spatial autoregressive model; Observation-level spatial interaction;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • D43 - Microeconomics - - Market Structure, Pricing, and Design - - - Oligopoly and Other Forms of Market Imperfection

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