Spurious Spatial Regression: Some Monte Carlo Results with a Spatial Unit Root and Spatial Cointegration

Abstract

In this paper I introduce the concepts of spatial unit roots and spatial cointegration, and via Monte‐Carlo simulation I illustrate their implications for spatial regression. It is shown that spatial unit roots lead to spurious (spatial) regression, as in the well‐known case involving time‐series. Spatial cointegration is similar to its time‐series counterpart, although I demonstrate that OLS estimation of spatial error‐correction models is not consistent.