Nonparametric identification of endogenous and heterogeneous aggregate demand models: Complements, bundles and the market level
Loading...
Date
2015
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
This paper studies nonparametric identification in market level demand models for
differentiated products. We generalize common models by allowing for the distribution
of heterogeneity parameters (random coefficients) to have a nonparametric distribution
across the population and give conditions under which the density of the random coef-
ficients is identified. We show that key identifying restrictions are provided by (i) a set
of moment conditions generated by instrumental variables together with an inversion of
aggregate demand in unobserved product characteristics; and (ii) an integral transform
(Radon transform) that maps the random coefficient density to the aggregate demand.
This feature is shown to be common across a wide class of models, and we illustrate this
by studying leading demand models. Our examples include demand models based on the
multinomial choice (Berry, Levinsohn, Pakes, 1995), the choice of bundles of goods that
can be substitutes or complements, and the choice of goods consumed in multiple units.