Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/289899 
Year of Publication: 
2023
Series/Report no.: 
Documento de Trabajo No. 318
Publisher: 
Universidad Nacional de La Plata, Centro de Estudios Distributivos, Laborales y Sociales (CEDLAS), La Plata
Abstract: 
Recent work on the conditional mean model offers the possibility of addressing misreporting of participation in social programs, which is common and has increased in all major surveys. However, researchers who employ quantile regression continue to encounter challenges in terms of estimation and statistical inference. In this work, we propose a simple two-step estimator for a quantile regression model with endogenous misreporting. The identification of the model uses a parametric first stage and information related to participation and misreporting. We show that the estimator is consistent and asymptotically normal. We also establish that a bootstrap procedure is asymptotically valid for approximating the distribution of the estimator. Simulation studies show the small sample behavior of the estimator in comparison with other methods, including a new three-step estimator. Finally, we illustrate the novel approach using U.S. survey data to estimate the intergenerational effect of mother's participation on welfare on daughter's adult income.
Subjects: 
Quantile regression
Misclassification
Endogenous Treatments
Survey data
JEL: 
C21
C25
I32
Document Type: 
Working Paper

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