There Is No Aggregation Bias: Why Macro Logit Models Work
研究嵌套Logit模型的加总性质，发现当消费者面临相同营销变量、品牌高度替代且价格分布不极端时，用加总数据拟合Logit模型在理论上成立，这解释了宏观Logit模型在商店扫描数据中的优异表现。
In this article, we examine the aggregation properties of (nested) logit models to understand their exceptional macro-level performance. The problem of aggregating micro logit models involves integrating nonlinear functions of model parameters over a distribution of consumer heterogeneity. The aggregation problem is analyzed using a mixture of analytic and simulation techniques, with the simulation experiments using actual panel data to calibrate the distribution of heterogeneity. We conclude that the practice of fitting aggregate logit models is theoretically justified under the following three conditions: (1) All consumers are exposed to the same marketing-mix variables, (2) the brands are close substitutes, and (3) the distribution of prices is not concentrated at an extreme value. These conditions are frequently met in store-level scanner data.