Local influence analysis in general spatial models

Abstract

We study the local influence in the general spatial model which includes the spatial autoregressive model and the spatial error model as two special cases. The stepwise local influence procedure is employed in our diagnostic analysis. We derive the local diagnostic measures in the general spatial model under three perturbation schemes, namely, the variance perturbation, dependent variable perturbation and explanatory variable perturbation schemes. A simulation example and two realdata examples are analysed in detail and they show that the stepwise local influence analysis is effective in identifying influential observations and is a powerful tool for uncovering masking effects.

Quantile Regression for Partially Linear Varying Coefficient Spatial Autoregressive Models

“…Lee and Yu (2010) proposed the maximum likelihood (ML) estimator for the spatial autoregressive (SAR) panel model with both spatial lag and spatial disturbances. Dai, et al (2015Dai, et al ( , 2016 respectively studied the local influence and outlier detection in the general spatial model which includes the spatial autoregressive model and the spatial error model as two special cases. Xu and Lee (2015) considered the instrumental variable (IV) and MLE estimators for spatial autoregressive model with a nonlinear transformation of the dependent variable.…”