Composite quantile regression estimation of linear error-in-variable models using instrumental variables

In this paper, we develop a composite quantile regression estimator of linear error-in-variable models based on instrumental variables. The proposed estimator is consistent and asymptotically normal under fairly general assumptions. It neither requires the measurement errors and the regression errors to have the same variance nor to belong to the same location-scale symmetric distribution. The simulation results show that the proposed method generates unbiased and efficient estimates for different types of the distributions of the regression errors in finite samples. An application to real data collected from the survey of identical twins to study the economic returns to schooling is also provided.