Nº 318 (Septiembre, 2023). Carlos Lamarche
“Quantile Regression with an Endogenous Misclassified Binary Regressor”.
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.
Códigos JEL: C21, C25, I32
Cita sugerida: Lamarche, C. (2023). Quantile Regression with an Endogenous Misclassified Binary Regressor. Documentos de Trabajo del CEDLAS Nº 318, Septiembre, 2023, CEDLAS-FCE-Universidad Nacional de La Plata.