Fortran must die
This is the first time I have heard of this technique [I think they are simply modifying linear regression]. They are concerned with what they call spatial issues, for example how rural versus urban influences their model [or the units that make up their model].
I have never encountered this before, can anyone point me in the direction they are talking about?
To address these concerns, spatial extensions of the fixed and random effects models were estimated in the context of the spatial autoregressive framework. Comparisons of out-of-sample predictive performance were again used to assess model performance. Spatial dependence was incorporated by adding an additional term to the models, a spatially lagged dependent variable of the form pWy.
Where, W was a row normalized n x n spatial weight matrix that represented the spatial connectivity among the various locations, p was the spatial dependence parameter representing the strength of the spatial dependence between neighboring observations and y was the dependent variable.