Shrinkage Estimation of Price and Promotional Elasticities: Seemingly Unrelated Equations
针对零售商或制造商基于超市扫描数据估计产品价格和促销弹性时面临的聚合困境，提出收缩估计方法，通过跨链和跨品牌借力，在减少估计变异性的同时保持灵活性，从而得到更合理且预测能力更强的弹性估计。
Abstract Consider the problem where a retailer or manufacturer wants to estimate product price and promotional elasticities based on supermarket scanner data. Classical linear modeling suffers from the following aggregation dilemma. Price and promotional elasticities appear to vary considerably among chains and brands so that one overall model is too restrictive. Alternatively, the use of a different model for each chain and brand leads to noisy and often nonsensical estimates of separate elasticities because of excessive data variation. To resolve this dilemma, shrinkage estimation procedures are proposed. By borrowing strength across chains and brands, these procedures reduce variability while providing flexibility that allows for separate elasticity estimates. Application of these procedures to a large data set yields not only more reasonable model estimates but also improved predictive power.