Nonparametric Identification of Differentiated Products Demand Using Micro Data
We examine identification of differentiated products demand when one has "micro data" linking individual consumers' characteristics and choices. Our model nests standard specifications featuring rich observed and unobserved consumer heterogeneity as well as product/market-level unobservables that introduce the problem of econometric endogeneity. Previous work establishes identification of such models using market-level data and instruments for all prices and quantities. Micro data provides a panel structure that facilitates richer demand specifications and reduces requirements on both the number and types of instrumental variables. We address identification of demand in the standard case in which non-price product characteristics are assumed exogenous, but also cover identification of demand elasticities and other key features when product characteristics are endogenous. We discuss implications of these results for applied work.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Nonparametric Identification of Random Coefficients in Endogenous and Heterogeneous Aggregate Demand Models
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 coeffi…
Identification of hedonic equilibrium and nonseparable simultaneous equations
This paper derives conditions under which preferences and technology are nonparametrically identified in hedonic equilibrium models, where products are differentiated along more than one dimension and agents are characte…
AttributeEstimating Discrete Choice Demand Models with Sparse Market-Product Shocks
We propose a new approach to estimating the random coefficient logit demand model for differentiated products when the vector of market-product level shocks is sparse. Assuming sparsity, we establish nonparametric identi…
counterfactualSufficient Statistics for Unobserved Heterogeneity in Structural Dynamic Logit Models
We study the identification and estimation of structural parameters in dynamic panel data logit models where decisions are forward-looking and the joint distribution of unobserved heterogeneity and observable state varia…
Penalized GMM Framework for Inference on Functionals of Nonparametric Instrumental Variable Estimators
This paper develops a penalized GMM (PGMM) framework for automatic debiased inference on functionals of nonparametric instrumental variable estimators. We derive convergence rates for the PGMM estimator and provide condi…