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

dc.contributor.authorLeSage, James P.
dc.contributor.authorVance, Colin
dc.contributor.authorChih, Yao-Yu
dc.date.accessioned2016-11-30T12:01:00Z
dc.date.available2016-11-30T12:01:00Z
dc.date.issued2016
dc.description.abstractWe 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.en
dc.identifier.urihttp://hdl.handle.net/2003/35678
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-17709
dc.language.isoende
dc.relation.ispartofseriesDiscussion Paper / SFB823;84, 2016en
dc.subjectspatial panel data modelsen
dc.subjectobservation-level spatial interactionen
dc.subjectspatial autoregressive modelen
dc.subjectMarkov chain Monte Carloen
dc.subject.ddc310
dc.subject.ddc330
dc.subject.ddc620
dc.titleA Bayesian heterogeneous coefficients spatial autoregressive panel data model of retail fuel duopoly pricingen
dc.typeTextde
dc.type.publicationtypeworkingPaperde
dcterms.accessRightsopen access

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