We apply local linear regression and sieve estimation technique to estimate functional
coefficients and an unknown spatial weighting function, respectively, via a nonparametric …

In this paper, we introduce the one-step generalized method of moments (GMM) estimation
methods considered in Lee (2007a) and Liu, Lee, and Bollinger (2010) to spatial models …

We propose a pairwise difference estimator for partially linear spatial autoregressive models
with heteroscedastic or/and spatially correlated error terms. In comparison with other …

Spatial statistics is a growing discipline providing important analytical techniques in a wide
range of disciplines in the natural and social sciences. In the R package GWmodel, we …

This article presents a partially linear additive spatial error model (PLASEM) specification
and its corresponding generalized method of moments (GMM). It also derives consistency …

The application of spatial econometrics techniques to microlevel data of firms or households
is problematic because of potentially large sample sizes and more-complicated spatial …

This paper investigates the finite sample properties of estimators for spatial dynamic panel
models in the presence of several endogenous variables. So far, none of the available …

spmlreg estimates the spatial lag, the spatial error, the spatial durbin, and the general spatial
(or spatial mixed) models by maximum likelihood. Optionally, at the end of the estimation …

Maximum likelihood (ML) estimation of spatial autoregressive models for large spatial data
sets is well established by making use of the commonly sparse nature of the contiguity …

Spatial regression models have recently received a lot of attention in a variety of fields to
address the spatial autocorrelation effect. One important class of spatial models is the …