Wild Bootstrap-Based Bias Correction for Spatial Quantile Panel Data Models with Varying Coefficients

This paper studies quantile regression for spatial panel data models with varying coefficients, taking the time and location effects of impacts covariates into account, i.e., implications may change over location. Smoothing methods are employed approximating including B-spline local polynomial approximation. A fixed-effects (FEQR) estimator is typically biased in presence lag variable. The wild bootstrap method to attenuate estimation bias. Simulations conducted study performance proposed show that stable efficient. Further, estimators based on perform much better than those approximation method, especially location-varying coefficients. Real about economic development China also analyzed illustrate application procedure.