Spatial Externalities, Spatial Multipliers, And Spatial Econometrics
This article outlines a taxonomy of spatial econometric model specifications that incorporate spatial externalities in various ways. The point of departure is a reduced form in which local or global spillovers are expressed as spatial multipliers. From this, a range of familiar and less familiar specifications is derived for the structural form of a spatial regression.
Spatial Econometric Models of Cross-Sectional Interdependence in Political Science Panel and Time-Series-Cross-Section Data
In this paper, we demonstrate the econometric consequences of different specification and estimation choices in the analysis of spatially interdependent data and show how to calculate and present spatial effect estimates substantively. We consider four common estimators—nonspatial OLS, spatial OLS, spatial 2SLS, and spatial ML. We examine analytically the respective omitted-variable and simultaneity biases of nonspatial OLS and spatial OLS in the simplest case and then evaluate the performance of all four estimators in bias, efficiency, and SE accuracy terms under more realistic conditions using Monte Carlo experiments. We provide empirical illustration, showing how to calculate and present spatial effect estimates effectively, using data on European governments' active labor market expenditures. Our main conclusions are that spatial OLS, despite its simultaneity, performs acceptably under low-to-moderate interdependence strength and reasonable sample dimensions. Spatial 2SLS or spatial ML may be advised for other conditions, but, unless interdependence is truly absent or minuscule, any of the spatial estimators unambiguously, and often dramatically, dominates on all three criteria the nonspatial OLS commonly used currently in empirical work in political science.
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