A Bayesian heterogeneous coefficients spatial autoregressive panel data model of retail fuel duopoly pricing
Loading...
Date
2016
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
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.
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 its duopoly rival station. 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.
Description
Table of contents
Keywords
spatial panel data models, observation-level spatial interaction, spatial autoregressive model, Markov chain Monte Carlo