Calculation and Visualization of Dynamic Price Elasticities

Abstract

Applying the Hierarchical Bayesian Regression model to weekly aggregated sales history data from 92 retail stores located around Tokyo, I calculated price elasticities by item, week, and store. These elasticities are more stable than figures calculated using the Hierarchical Regression or Bayesian Regression models. Furthermore, using Google Earth, I visualized the calculated price elasticities of these 92 stores over 67 weeks, providing a better understanding about heterogeneity of price elasticities across time and space. Keywords: Price Elasticity, Hierarchical Bayesian Regression Model, Markov Chain Monte Carlo (MCMC) method, Google Earth To cite this document: Hirokazu Tajima, "Calculation and Visualization of Dynamic Price Elasticities", Contemporary Management Research, Vol.9, No.4, pp.389-398, 2013. Permanent link to this document: http://dx.doi.org/10.7903/cmr.11071