Nonparametric identification of random coefficients in aggregate demand models for differentiated products

被引：1

作者：

Dunker, Fabian ^{[1
]}

Hoderlein, Stefan ^{[2
]}

Kaido, Hiroaki ^{[3
]}

机构：

[1] Univ Canterbury, Sch Math & Stat, Private Bag 4800, Christchurch 8140, New Zealand

[2] Emory Univ, Dept Econ, 1602 Fishburn Dr, Atlanta, GA 30322 USA

[3] Boston Univ, Dept Econ, 270 Bay State Rd, Boston, MA 02215 USA

来源：

ECONOMETRICS JOURNAL | 2023年 / 26卷 / 02期

关键词：

Random coefficients; aggregate demand; nonparametric identification; DISTRIBUTIONS; VARIABLES; EQUATIONS;

D O I：

10.1093/ectj/utad002

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

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 the fractions of individuals who benefit from a counterfactual intervention, is identified.

引用

页码：279 / 306

页数：28

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