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...

One of the central challenges to inference in the context of potentially interdependent observations, known as Galton's Problem, is the difficulty distinguishing spatially correlated observations due to observed units exposure to spatially correlated shocks from spatial correlation in outcomes...

Although scholars recognize that time-series-cross-section data typically correlate across both time and space, they tend to model temporal dependence directly, often by lags of dependent variables, but to address spatial interdependence solely as a nuisance to be "corrected" by FGLS or to which...