Spatial autoregressions with an extended parameter space and similarity-based weights

We provide in this paper asymptotic theory for a spatial autoregressive model (SAR, henceforth) in which the spatial coefficient is allowed to be less than or equal to unity, as well as consistent with a local to unit root (LUR) model and of the moderate integration (MI) from unity type, and the spatial weights are allowed to be similarity-based and data driven.

Other special cases of our setting include the random walk, a model in which all the weights are equal, the standard SAR model in which the spatial parameter is strictly less than unity and the similarity based autoregression in which the spatial parameter equals unity and data do not display a natural order.

We resort to random norming to treat all cases in a uniform manner and we use a shifted profile likelihood to obtain results which are valid for all cases. A small simulation experiment supports our findings and the usefulness of our model is illustrated with an empirical application of theBoston housing data set. 

Zoom Link: https://univr.zoom.us/j/89754173476