Identification of a Heterogeneous Generalized Regression Model with Group Effects
21 Pages Posted: 12 Oct 2009
Date Written: October 8, 2009
Abstract
We consider identification in a "generalized regression model" (Han, 1987) for panel settings in which each observation can be associated with a "group" whose members are subject to a common unobserved shock. Common examples of groups include markets, schools or cities. The model is fully nonparametric and allows for the endogeneity of group-specific observables, which might include prices, policies, and/or treatments. The model features heterogeneous responses to observables and unobservables, and arbitrary heteroskedasticity. We provide sufficient conditions for full identification of the model, as well as weaker conditions sufficient for identification of the latent group effects and the distribution of outcomes conditional on covariates and the group effect.
Keywords: nonparametric identification, binary choice, threshold crossing, censored regression, proportional hazard model
JEL Classification: C23, C24, C25
Suggested Citation: Suggested Citation
Do you have negative results from your research you’d like to share?
Recommended Papers
-
Nonparametric Identification of Multinomial Choice Demand Models with Heterogeneous Consumers
By Steven Berry and Philip A. Haile
-
Nonparametric Identification of Multinomial Choice Demand Models with Heterogeneous Consumers
By Steven Berry and Philip A. Haile
-
Empirical Industrial Organization: A Progress Report
By Liran Einav and Jonathan Levin
-
Identification in Differentiated Products Markets Using Market Level Data
By Steven Berry and Philip A. Haile
-
Identification in Differentiated Products Markets Using Market Level Data
By Steven Berry and Philip A. Haile
-
Identification in Differentiated Products Markets Using Market Level Data
By Steven Berry and Philip A. Haile
-
The Random Coefficients Logit Model is Identified
By Patrick Bajari, Jeremy T. Fox, ...