Spatial-Econometric Analysis of Interdependence, 5 cr

Often in social science research outcomes in some units or individuals in your research analyses depend on outcomes in other units. Outcomes in your research studies likely have influences from neighboring units. Do the processes you study diffuse or spillover across units in some manner? 

This introductory course teaches empirical methods utilizing spatial-autoregressive models for modeling, estimation, and interpretation of spatial effects also known as spatial, or cross-unit interdependence involving contagion, diffusion/spillover. Lectures, applied lab sessions and exercises utilize GeoDa software, however students are welcome to use other software preferences, R Stata etc.. Spatial interdependence is a common consideration in social sciences research. Outcomes in one observational unit are almost always related to similar occurrences in other observational units. 

Social science with spatial considerations cover disturbances and conflicts within and among nations, interactions between consumer, investor, and producer interactions in markets, societal behavior relating to crime, health, and economic characteristics, voting and elections and policies across political jurisdictions. Standard statistical methods which assume independent observations are inadequate. 

This course introduces methodological strategies appropriate for interdependent observations, emphasizing spatial and spatiotemporal models of interdependent continuous and limited outcomes.