2021

Research / 2021

Research

Quantile Regression for Varying Coefficient Spatial Error Models

2021.01.01

Publication Time2021.01.01

Lead AuthorXiaowen Dai

Corresponding AuthorMaozai Tian

Journal】 Communications in Statistics - Theory and Methods


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.


Keywords

Spatial error models,varying coefficientquantile regressionlocal polynomial approximation