Nonparametric Identification of Random Coefficients in Endogenous and Heterogeneous Aggregate Demand Models

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Tác giả: Fabian Dunker, Stefan Hoderlein, Hiroaki Kaido

Ngôn ngữ: eng

Ký hiệu phân loại: 003.1 System identification

Thông tin xuất bản: 2022

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Bộ sưu tập: Metadata

ID: 168574

This paper studies nonparametric identification in market level demand models for differentiated products with heterogeneous consumers. We consider a general class of models that allows for the individual specific coefficients to vary continuously across the population and give conditions under which the density of these coefficients, and hence also functionals such as welfare measures, is identified. A key finding is that two leading models, the BLP-model (Berry, Levinsohn, and Pakes, 1995) and the pure characteristics model (Berry and Pakes, 2007), require considerably different conditions on the support of the product characteristics.
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