Host Institution
ICPSR and the University of Michigan

Instructor
Luc Anselin
University of Illinois, Urbana-Champaign

The Workshop
Spatial regression analysis or spatial econometrics is the collection of statistical and econometric methods specifically geared at dealing with problems of spatial dependence and spatial heterogeneity encountered in cross-sectional (and panel) data sets. The use of spatial econometric techniques is increasingly common in empirical work in the social sciences, including urban and regional economics, criminology, demography and electoral analyses.

The main objective of the course is to review the state of the art of the specification, estimation and testing of models that incorporate spatial dependence (and spatial heterogeneity). While the focus will be on spatial aspects, the types of methods covered have general validity in applied statistical work.

The course will include topics such as the specification of dependent stochastic processes (specifically, various types of spatial autoregressive models), maximum likelihood estimation of dependent processes, instrumental variables and general method of moments estimation, specification tests, and asymptotic and finite sample properties. While most of the material will be applied to the standard regression model, some attention will be paid to panel data contexts (space-time models) as well as to spatial probit models. An important aspect of the course is the application of the spatial regression techniques in empirical practice, using the SpaceStat software package or general purpose statistical toolboxes.

Prerequisites include intermediate regression analysis (or intermediate econometrics) as well as familiarity with spatial data analysis at the level of ICPSR's "Introduction to Spatial Data Analysis" course.

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