A Bayesian Approach to Estimating Household Parameters
A Bayesian Approach to Estimating Household Parameters
复制标题
估计家庭参数的贝叶斯方法
DOI：
10.1177/002224379303000204
复制
发表时间：
1993
影响因子：
6.1
通讯作者：
Greg M. Allenby
中科院分区：
文献类型：
--
作者：
Peter E. Rossi;Greg M. Allenby
作者提出了一种贝叶斯方法来估计家庭参数。应用到标准的logit模型，该程序产生的所有模型参数的家庭一级的估计，使研究人员能够确定家庭的反应在营销组合中的所有变量的差异。模拟数据被用来研究估计的小样本性能。估计器可以很容易地实现与标准算法用于最大化似然函数。在金枪鱼扫描仪面板数据的应用中，检测到价格，显示和功能响应（斜率）参数的异质性的强有力的证据。不考虑斜率异质性的方法被证明低估了该数据集中功能广告和店内展示的价值。此外，家庭价格敏感性估计与优惠券的使用密切相关，并展示了如何使用估计来实现有针对性的家庭下降。
The authors present a Bayesian approach to the estimation of household parameters. Applied to the standard logit model, the procedure produces household-level estimates of all model parameters, enabling researchers to identify differences in household reaction to all variables in the marketing mix. Simulated data are used to study the small-sample performance of the estimator. The estimator can be easily implemented with standard algorithms used to maximize likelihood functions. In application to tuna scanner panel data, strong evidence of heterogeneity in price, display, and feature response (slope) parameters is detected. Approaches that fail to take into account slope heterogeneity are shown to underestimate the value of feature advertising and in-store displays in this dataset. In addition, the household price sensitivity estimates are strongly related to coupon usage and demonstrate how the estimates can be used to implement a targeted household drop.