While estimates of models with spatial interaction are very sensitive to the choice of spatial weights, considerable uncertainty surrounds de nition of spatial weights in most studies with cross-section dependence. We show that, in the spatial error model the spatial weights matrix is only...

While estimates of models with spatial interaction are very sensitive to the choice of spatial weights, considerable uncertainty surrounds the definition of spatial weights in most studies with cross-section dependence. We show that, in the spatial error model, the spatial weights matrix is only...

While estimates of models with spatial interaction are very sensitive to the choice of spatial weights, considerable uncertainty surrounds definition of spatial weights in most studies with cross-section dependence. We show that, in the spatial error model the spatial weights matrix is only...

This paper proposes a methodology for estimation of spatial weights matrices which are consistent with a given or estimated pattern of spatial autocovariance. This approach is potentially useful for applications in urban, environmental, development, growth and other areas of economics where...

Functional magnetic resonance imaging (fMRI) has become the standard technology in human brain mapping. Analyses of the massive spatio-temporal fMRI data sets often focus on parametric or nonparametric modeling of the temporal component, while spatial smoothing is based on Gaussian kernels or...