Retail gasoline pricing: A Bayesian hierarchical approach to modeling the effect of brand on elasticity

Retail gasoline demand can be modelled using proprietary site level sales data for one operator or aggregate market share data covering all brands within a region. We show how to incorporate standard demand models for these situations into Bayesian hierarchies in which the effect of brand on price elasticity can be estimated. For site level data, we show how to estimate brand effect on the distribution of competitor cross elasticities and, in the case of multi‐branded operators, on the distribution of direct elasticities. For market share data, we show how to estimate regional average direct elasticities by brand.