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

Fabian Dunker (), Stefan Hoderlein and Hiroaki Kaido

Papers from arXiv.org

Abstract: 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.

Date: 2022-01
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http://arxiv.org/pdf/2201.06140 Latest version (application/pdf)

Related works:
Working Paper: Nonparametric identification of random coefficients in endogenous and heterogeneous aggregate demand models (2017)
Working Paper: Nonparametric identification of random coefficients in endogenous and heterogeneous aggregate demand models (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2201.06140

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