Estimating Product-Level Price Elasticities Using Hierarchical Bayesian

This post introduces hierarchical Bayesian modeling for estimating product-level price elasticities. The method incorporates data from various groupings to improve the estimation of individual-level effects, which is particularly beneficial for products with limited data. The article describes the modeling process, challenges of traditional approaches, and benefits of hierarchical Bayesian models, supported by Python code using the Numpyro library. Various applications of the method in retail demand forecasting and recommendation systems are also discussed.