QUANTILE REGRESSION WITH MISMEASURED COVARIATES
证明工具变量能识别并一致估计存在测量误差的非参数分位数回归模型，提出基于条件期望导数的非线性估计量，并证明其在紧集上一致收敛。
This paper establishes that the availability of instrumental variables enables the identification and the consistent estimation of nonparametric quantile regression models in the presence of measurement error in the regressors. The proposed estimator takes the form of a nonlinear functional of derivatives of conditional expectations and is shown to provide estimated quantile functions that are uniformly consistent over a compact set.