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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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