The instrumental variable quantile regression (IVQR) model (Chernozhukov and Hansen, 2005) is a popular tool for …-convexity of the IVQR GMM objective function. This paper shows that the IVQR estimation problem can be decomposed into a set of … conventional quantile regression sub-problems which are convex and can be solved efficiently. This reformulation leads to new …

The instrumental variable quantile regression (IVQR) model of Chernozhukov and Hansen (2005, 2006) is a flexible and …. Estimation, however, is computationally burdensome because the GMM objective function is non-smooth and non-convex. This paper … shows that the IVQR estimation problem can be decomposed into a set of conventional quantile regression sub-problems, which …

. Efficient estimators and uniformly valid confidence intervals for regression coefficients on target variables (e.g., treatment … or policy variable) in a high-dimensional approximately sparse regression model, for average treatment effect (ATE) and … heteroscedastic and non-Gaussian errors are implemented. Moreover, joint/ simultaneous confidence intervals for regression …

increasingly important in applied econometrics. As with mean-regression models, an erroneous assumption that the explanatory … whose structural function is identified through a conditional quantile restriction. Quantile regression models are … variables in a quantile regression model are exogenous can lead to highly misleading results. In addition, a test of exogeneity …