The Inverse Product Differentiation Logit Model

• 成果类型: Article
• 署名作者: Fosgerau, Mogens; Monardo, Julien; De Palma, Andre
• 署名单位: University of Copenhagen; University of Bristol; CY Cergy Paris Universite
• 刊物名称: AMERICAN ECONOMIC JOURNAL-MICROECONOMICS
• ISSN/ISSBN: 1945-7669
• DOI: 10.1257/mic.20210066
• 发表日期: 2024
• 关键词: random-coefficients logit; discrete-choice models; nested logit; market power; demand; price; identification; segmentation; COMPETITION; mergers

摘要

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)