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A Bayesian heterogeneous coefficients spatial autoregressive panel data model of retail fuel price rivalry

Author

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

Abstract

We apply a heterogenous coefficient spatial autoregressive panel model from Aquaro, Bailey and Pesaran (2015) to explore competition/cooperation by Berlin fueling stations in setting prices for diesel and E5 fuel. Unlike the maximum likelihood estimation method set forth by Aquaro, Bailey and Pesaran (2015), we rely on a Markov Chain Monte Carlo (MCMC) estimation methodology. MCMC estimates as applied here with non-informative priors will produce estimates equal to those from maximum likelihood, a point we demonstrate with a Monte Carlo experiment. We explore station-level price mark-ups using over 400 fueling stations located in and around Berlin, average daily diesel and E5 fuel prices, and refinery cost information covering more than 487 days. The heterogeneous coefficients spatial autoregressive panel data model uses the large sample of daily time periods to produce spatial autoregressive model estimates for each fueling station. These estimates provide information regarding the price reaction function of each station to neighboring stations. This is in contrast to conventional estimates of price reaction functions that average over the entire cross-sectional sample of stations. We show how these estimates can be used to infer competition versus cooperation in price setting by individual stations. The empirical results reveal a mix of competitive and collusive price setting, with some evidence that stations located near others of the same brand tend toward collusion, while those located near rival brands tend toward competition.

Suggested Citation

  • Lesage, James P. & Vance, Colin & Chih, Yao-Yu, 2016. "A Bayesian heterogeneous coefficients spatial autoregressive panel data model of retail fuel price rivalry," Ruhr Economic Papers 617, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    • Handle: RePEc:zbw:rwirep:617
    DOI: 10.4419/86788717
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    References listed on IDEAS

    as
    1. Kelejian, Harry H. & Prucha, Ingmar R., 2010. "Specification and estimation of spatial autoregressive models with autoregressive and heteroskedastic disturbances," Journal of Econometrics, Elsevier, vol. 157(1), pages 53-67, July.
    2. James P. LeSage & R. Kelley Pace, 2014. "The Biggest Myth in Spatial Econometrics," Econometrics, MDPI, vol. 2(4), pages 1-33, December.
    3. Manuel Frondel & Colin Vance & Alex Kihm, 2016. "Time lags in the pass-through of crude oil prices: big data evidence from the German gasoline market," Applied Economics Letters, Taylor & Francis Journals, vol. 23(10), pages 713-717, July.
    4. Dieter Pennerstorfer, 2009. "Spatial price competition in retail gasoline markets: evidence from Austria," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 43(1), pages 133-158, March.
    5. Haucap, Justus & Heimeshoff, Ulrich & Siekmann, Manuel, 2015. "Price dispersion and station heterogeneity on German retail gasoline markets," DICE Discussion Papers 171, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    6. repec:zbw:rwirep:0522 is not listed on IDEAS
    7. Michele Aquaro & Natalia Bailey & M. Hashem Pesaran, 2015. "Quasi Maximum Likelihood Estimation of Spatial Models with Heterogeneous Coefficients," Working Papers 749, Queen Mary University of London, School of Economics and Finance.
    8. Mobley, Lee R., 2003. "Estimating hospital market pricing: an equilibrium approach using spatial econometrics," Regional Science and Urban Economics, Elsevier, vol. 33(4), pages 489-516, July.
    9. J. Paul Elhorst & Sandy Fréret, 2009. "Evidence Of Political Yardstick Competition In France Using A Two‐Regime Spatial Durbin Model With Fixed Effects," Journal of Regional Science, Wiley Blackwell, vol. 49(5), pages 931-951, December.
    10. Michele Aquaro & Natalia Bailey & M. Hashem Pesaran, 2015. "Quasi Maximum Likelihood Estimation of Spatial Models with Heterogeneous Coefficients," Working Papers 749, Queen Mary University of London, School of Economics and Finance.
    11. Maarten Allers & J. Elhorst, 2005. "Tax Mimicking and Yardstick Competition Among Local Governments in the Netherlands," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 12(4), pages 493-513, August.
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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. Ge, S., 2020. "Text-Based Linkages and Local Risk Spillovers in the Equity Market," Cambridge Working Papers in Economics 20115, Faculty of Economics, University of Cambridge.

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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
    • L11 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Production, Pricing, and Market Structure; Size Distribution of Firms

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