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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. (JEL C25, D11, D12, L66, L81)
Fosgerau et al. (Tue,) studied this question.