Modeling Spatial Externalities: A Panel Data Approach

In this paper we argue that the Spatial Durbin Model (SDM) is an appropriate framework to empirically quantify different kinds of externalities. Besides, it is also attractive from an econometric point of view as it nests several other models frequently employed. Up to now the SDM was applied in cross-sectional settings only, thereby ignoring individual heterogeneity. This paper extends the SDM to panel data allowing for non-spherical disturbances and proposes an estimator based on ML techniques. Results from a Monte Carlo study reveal that the estimator has satisfactory small sample properties and that neglecting the non-spherical nature of the errors leads to inflated standard errors. Moreover, we show that the incidence of type two errors in testing procedures for parameter significance of spatially lagged variables is the higher the denser the spatial weight matrix.