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.
This is a preview of subscription content, log in to check access.
Amrhein CG (1995) Searching for the elusive aggregation effect: evidence from statistical simulations. Environ Plan A 27:105–119CrossRefGoogle Scholar
Openshaw S, Taylor PJ (1979) A million or so correlation coefficients: three experiment on the modifiable areal unit problem. In: Wrigley N (ed) Statistical applications in the spatial sciences. Pion, London, pp 127–144Google Scholar
Páez A, Farber S, Wheeler D (2011) A simulation-based study of geographically weighted regression as a method for investigating spatially varying relationships. Environ Plan A 43(12):2992–3010CrossRefGoogle Scholar
Pesaran MH (2006) Estimation and inference in large heterogeneous panels with a multifactor error structure. Econometrica 74:967–1012CrossRefGoogle Scholar
Pesaran MH (2015) Time series and panel data econometrics. Oxford University Press, OxfordCrossRefGoogle Scholar