ABSTRACT

This paper sets out a general framework for store sales evaluation and prediction. The sales of a retail chain with multiple stores are first decomposed into five components, and then each component is explained by store, competitor and consumer characteristics using random effects models for components observable at the store level and spatial error random effects models for components observable at the zip code level. We use spatial panel data over four years for estimation and a subsequent year for evaluating one-year-ahead predictions. Set against a benchmark model that explains total sales directly, the prediction error of our framework is reduced by 34% for existing stores during the sample period, by 5% for existing stores one year ahead and by 26% for new stores.

摘要

本文提出了商店销售评估和预测的一般框架。首先将具有多个商店的零售连锁店的销售额分解为五个分量，然后使用商店级别可观察组件的随机效应模型和可观察组件的空间误差随机效应模型将每个组件解释为商店、竞争对手和消费者特征。邮政编码级别。我们使用超过四年的空间面板数据进行估计，并使用随后一年来评估提前一年的预测。与直接解释总销售额的基准模型相比，我们框架的预测误差在样本期间对现有商店减少了 34%，对现有商店提前一年减少了 5%，对新商店减少了 26%。