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 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 and specification tests. 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. The course is a short version of the graduate spatial econometrics course offered at the University of Illinois.
An important aspect of the course is the application of the spatial regression techniques in empirical practice, using the SpaceStat™ software package or other software tools.
Prerequisites include familiarity with regression analysis at the level of an intermediate graduate econometrics course, and knowledge of spatial data analysis at the level of ICPSR's Introduction to Spatial Data Analysis course. If you don't meet these prerequisites, it is strongly recommended that you take a more introductory level course first.
This course will be held as part of the regular ICPSR Summer Program in Ann Arbor, MI.
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