Analysis of spatially dependent data

A variety of studies that relate both to the further theoretical development of spatial models, and their application to various economic issues are discussed. Baltagi, Song, Jung, and Koh consider a spatial panel data model which involves time series autocorrelation, as well as spatial dependence between spatial units at each point in time. The model also allows for heterogeneity of spatial units through random effects. The paper by Kapoor, Kelejian, and Prucha considers a panel data model where the disturbances have an error component structure. Kelejian and Prucha suggest a nonparametric heteroscedasticity and autocorrelation consistent (HAC) estimator for an asymptotic variance covariance (VC) matrix which would naturally arise in a spatial framework in which an instrumental variable (IV) procedure is used to estimate the model parameters. The paper by Lee considers the estimation of a mixed regressive spatial autoregressive model, which introduces a computationally simple generalized method of moments (GMM) for the estimation of this model.