Weak and strong cross-sectional dependence are discussed. Various spatial approaches to model cross-sectional dependence, such as the spatial error or spatial regressive approach for static or dynamic models, are introduced. Issues of endogeneity of spatial weight matrix, higher-order spatial weight matrix, and the mixed spatial and factor process are considered. The Lagrangian multiplier and CD tests for cross-sectional uncorrelation for various types of models are also discussed.

This chapter deals with

• Spatial econometrics notions

• Handling spatial dependence and spatial heterogeneity through spatial econometric models

• Diagnostics for spatial dependence (Lagrange multiplier tests)

• Spatial autoregressive model (SAR)

• Spatial error model

• Spatial filtering

• Spatial regression with regimes

• Spatial two-stage least squares (S2SLS)

• Maximum likelihood

• Generalized method of moments (GMM)

After a thorough study of the theory and lab sections, you will be able to

• Distinguish which spatial regression method to use in order to account for spatial dependence and spatial heterogeneity

• Interpret spatial diagnostics used in OLS regression that can guide you to the appropriate spatial regression model

• Build spatial regression models and interpret results

• Identify if spatial dependence or spatial heterogeneity is accounted for by the adopted method

• Conduct spatial econometric analysis through GeoDa space