Spatial econometrics with cross section and panel data

Instructor: Jeffrey M. Wooldridge, Distinguished Professor, Michigan State University, USA

Topics covered in ASSEE 2020 included:

• The nature of spatial correlation.
• Policy analysis with spatial structures.
• Ordinary Least Squares estimation of spatial models and robust standard errors.
• Models with endogenous explanatory variables and instrumental variables.
• Generalized Method of Moments estimation.
• Linear panel data models with unobserved heterogeneity.
• Fixed Effects, Random Effects, Correlated Random Effects estimation with spatial data.
• Instrumental variables methods for spatial panel data.
• Maximum Likelihood estimation, partial MLE, Generalized estimating equations.
• Models with strictly exogenous explanatory variables.
• Lagged dependent variables with spatial correlation.

Description

The course focused on methods for exploiting and modeling spatial correlation in econometric applications. It covered both cross-sectional applications and panel data. Coverage began with linear models, allowing for exogenous and endogenous explanatory variables. But it also covered methods for estimating nonlinear models with spatial features. Along the way methods for cluster samples were used as simple alternatives that exploit spatial structures, yet are computationally simple.