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

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.