Spatial Econometrics


  • Robert J. Franzese, University of Michigan

Cross-unit (i.e., "spatial") interdependence is ubiquitous throughout the social sciences, and beyond. Events or outcomes in one observational unit are almost always related to similar occurrences in other observational units. This is so for such diverse phenomena as disturbances and conflicts within and among nations; crime, health, and environmental outcomes; policies in political jurisdictions; consumer, investor, and producer choices in markets; individuals’ opinions and behavior in societies; and voting by citizens in elections or by legislators in legislatures. In such contexts, "standard" statistical methods (which assume independent observations) are inappropriate, and design-based methods of "nonparametric causal-inference" are, at best, inadequate. This workshop introduces strategies appropriate for interdependent observations, emphasizing spatial and spatiotemporal models of interdependent continuous and limited outcomes.

The main objective of the workshop is to demonstrate how such spatial, cross unit, interdependence can be incorporated into empirical analysis most productively. Course participants will learn how to: diagnose spatial-correlation patterns; estimate spatial-regression models; distinguish between different sources of spatial correlation (common exposure, contagion, and selection); and calculate and present the spatial and spatiotemporal effects that empirical models which incorporate interdependence imply. Methods to be covered include: measures of spatial association; instrumental-variable and maximum-likelihood estimators for regression models with spatial interdependence; multiple-spatial-lag models; spatial interdependence in models with limited and qualitative dependent-variables; and models for coevolutionary processes.

Prerequisites: Participants in this course should be familiar with linear regression and models for qualitative/limited dependent variables (e.g., logit, probit, etc.). This workshop does not assume any prior knowledge of, or experience with, spatial statistics.

NOTE: For purposes of this course, the term "spatial" means "among spatial units"; the correlation and/or dependence in question may or may not be geographically based.

Fee: Members = $1700; Non-members = $3200

Tags: spatial econometrics, interdependent observations, spatial interdependence, interdependence, contagion, spillovers, diffusion

Course Sections

Section 1

Location: ICPSR -- Ann Arbor, MI

Date(s): July 8 - July 12

Time: 9:00 AM - 5:00 PM


  • Robert J. Franzese, University of Michigan