Incorporating heterogeneity with store-level aggregate data
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This paper introduces a methodology to incorporate heterogeneity in the analysis of store level aggregate data. The proposed model is validated using two sets of scanner panel data, for tuna and ketchup, and their corresponding weekly aggregate data. The model recovers the true parameters with acceptable accuracy.
The model has several advantages over the previous aggregate models, such as the linear model, the semilog model, and the log-log model. First, the cross-price elasticities estimated from the model show the asymmetric responses to the price promotions very close to those from the logit model applied to the panel data. Second, the model shows better prediction performance.
Key wordsHeterogeneity aggregation bias random coefficient models
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