Nonparametric identification of random coefficients in endogenous and heterogeneous aggregate demand models
Year: 2017
Type: report
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.
Authors: Fabian Dunker, Stefan Hoderlein, Hiroaki Kaido
Institutions: Boston University, Boston College, University of Göttingen
Language: English
Subfield: Marketing
Domain: Social Sciences
Open Access status: green
Awards: G503192557, G6556031174
OpenAlex ID: W2591192376