Spatial Data Analysis and Econometrics

Key developments in the econometric analysis of spatial cross-section data are reviewed. The spatial connectivity matrix (W) is introduced and its implications for spatial autocorrelation (SAC) is explained. Alternative statistical tests for spatial autocorrelation are reviewed. The spatial autoregression model (SAR) is introduced and its relation to regression models with spatial lagged dependent variables is explained. A common factor test is described, which tests the hypothesis that SAC is induced by the omission of spatial lagged dependent variables. Alternative estimation methods for spatial lag models are compared and contrasted, including maximum likelihood and instrumental variable methods. Spatial statistical methods such as spatial principal components generated by W, spatial filtering and geographically weighted regression are reviewed. The fundamental differences between spatial data and time series data are emphasized. Time is inherently sequential whereas space is not. Time is potentially infinite whereas space is not. Time has a natural unit of measurement (hours, months, years) whereas space does not. The MAUP (modifiable area unit problem) is discussed, which arises because, unlike physical space, socioeconomic space does not have a natural unit of measurement.