Despite the huge availability of software to estimate cross-sectional spatial models, there are
only few functions to estimate models dealing with spatial limited dependent variable. This …

Using approximations of the score of the log-likelihood function, we derive moment
conditions for estimating spatial regression models, starting with the spatial error model. Our …

The present paper suggests an estimation procedure for a Cliff and Ord type spatial panel
data model with random effects. Building on existing literature, the paper suggests an …

This paper studies the generalized method of moment (GMM) estimation of partially linear
single-index spatial autoregressive model (PLSISARM). The asymptotic normality of the …

Heteroskedasticity-robust semi-parametric GMM estimation of a spatial model with space-
varying coefficients. Spatial Economic Analysis. The spatial model with space-varying …

Use of multiplicative interaction of explanatory variables has been a standard practice in the
regression modeling literature, and estimation of the parameters of such a model in the case …

This paper first presents simple methods for conducting up to third-order bias and variance
corrections for the quasi maximum likelihood (QML) estimators of the spatial parameter (s) in …

Recent advances in the implementation of spatial econometrics model estimation
techniques have made it desirable to compare results, which should correspond between …

The spatial autoregressive (SAR) model is a standard tool for analyzing data with spatial
correlation. Conventional estimation methods rely on the key assumption that the spatial …

Geographically weighted regression (GWR), as a useful method for exploring spatial non-
stationarity of a regression relationship, has been applied to a variety of areas. In this …