Quantile regression with an endogenous misclassified binary regressor - Carlos Lamarche (Universidad Nacional de La Plata, Centro de Estudios Distributivos, Laborales y Sociales (CEDLAS), 2023)
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.
Keywords: C21; C25; I32; 330; Quantile regression; Misclassification; Endogenous Treatments; Survey data