The Inverse Product Differentiation Logit Model
提出逆产品差异化Logit模型，用于估计差异化产品市场的需求，能捕捉市场细分和互补性，可用市场层面数据通过线性工具变量回归估计，并通过蒙特卡洛实验和即食麦片数据验证其表现。
We introduce the inverse product differentiation logit (IPDL) model, a micro-founded inverse market share model for differentiated products that captures market segmentation according to one or more characteristics. The IPDL model generalizes the nested logit model to allow richer substitution patterns, including complementarity in demand, and can be estimated by linear instrumental variable regression with market-level data. Furthermore, we provide Monte Carlo experiments comparing the IPDL model to the workhorse empirical models of the literature. Lastly, we demonstrate the empirical performance of the IPDL model using a well-known dataset on the ready-to-eat cereal market.