Spatial Econometrics: Empirical Analysis of Geospatial Association and Cross-Unit Interdependence


  • Robert J. Franzese, University of Michigan

This workshop will be offered in an online video format.

Spatial (i.e., geospatial or otherwise cross-unit) association and interdependence are ubiquitous throughout the social sciences, and beyond. That is, 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; economic and other 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 contexts where this omnipresent cross-unit association (or correlation) arises from interdependence (or contagion), "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 distinguishing spatial association from spatial interdependence and for proper estimation of processes involving 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, i.e. geo-spatial or otherwise 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; models and methods for (exogenous) spatial correlation; instrumental-variable and maximum-likelihood estimators for models with (endogenous) spatial interdependence; multiple-spatial-lag models; spatial interdependence in models with limited and qualitative dependent-variables; and models for coevolutionary processes (i.e., processes with both spatial-cum-network interdependence and endogenous-connectivity/network-selection).

Prerequisites: None; in particular, although participants 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. Indeed, all necessary mathematical, statistical, geospatial-analytic, and spatial-econometric background will be reviewed as needed, albeit (obviously) very quickly.

Registration Fee: Members = $1,500; Non-members = $3,000

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

Course Sections

Section 1

Location: Online -- Video format,

Date(s): July 20 - July 24

Time: 9:00 AM - 5:00 PM


  • Robert J. Franzese, University of Michigan