Quantile regression for varying coefficient spatial error models

Abstract

This paper investigates the quantile regression estimation for spatial error models with possibly varying coefficients. The local polynomial fitting scheme is employed to approximate the varying coefficients. The rank-based score test is developed for hypotheses on the model and the constancy of the varying coefficients. The asymptotic properties of the proposed estimators and test statistics are both established. Monte Carlo simulations are conducted to study the finite sample performance of the proposed method. Analysis of a real data example is presented for illustration.