ICPSR 5-day workshop on Spatial-Econometric Analysis of Interdependence, July 9-13 at the University of Michigan - Ann Arbor
Space is still available for the ICPSR 5-day workshop on Spatial-Econometric Analysis of Interdependence, July 9-13 at the University of Michigan - Ann Arbor.
Instructor(s): Robert J. Franzese, University of Michigan
Do the outcomes in some units or individuals in your research analyses depend on outcomes in other units? That is, are the outcomes of interest in your studies likely contagious from units to neighboring units, or otherwise proximate or connected units? Do the processes you study diffuse across units in some manner? Are there spillovers across subjects? If you study anything in the social sciences, and likely most things beyond, almost certainly they are/do.
This workshop (July 9-13 in Ann Arbor) teaches empirical methods (focusing on spatial-autoregressive models) for modeling, for estimation, and for interpretation of such spatial (i.e., cross-unit) interdependence (a.k.a., contagion/diffusion/spillover/network-dependence...).
Applied (lab) sessions and exercises are bilingual, with lab scripts in Stata and R both available, and students are of course welcome to use other software as they prefer.
Register through the ICPSR Summer School portal, linked here:
The course description from the ICPSR website is linked here & copied below:
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; consumer, investor, and producer choices in markets; individuals' opinions and behavior in societies; crime, health, and environmental outcomes; voting by citizens in elections or by legislators in legislatures; and policies in political jurisdictions. In such contexts, "standard" statistical methods (which assume independent observations) are inappropriate, and design-based methods of "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.
Tags: spatial econometrics, interdependent observations, spatial interdependence, interdependence, contagion, spillovers, diffusion
Location: ICPSR -- Ann Arbor, MI
Date(s): July 9 - July 13
Time: 9:00 AM - 5:00 PM