Bootstrap Test Statistics for Spatial Econometric Models

We introduce and apply bootstrap method for testing spatial correlation in a linear regression model. Given the consideration of a fixed spatial structure and heteroscedasticity of unknown form in the data, spatial bootstrap is a hybrid of recursive wild bootstrap method. Based on the Moran’s index I, two versions of LM-Error and LM-Lag statistics, we demonstrate that the spatial bootstrap procedure can be used for model identification (pre-test) and diagnostic checking (post-test) of a spatial econometric model. With two empirical examples, the bootstrap method is proven to be an effective alternative to the theoretical asymptotic approach for hypothesis testing when the distribution assumption is violated