3H Spatial Econometrics
Jude Hays, University of Pittsburgh
6 - 17 August (two week course / 35 hrs)
Detailed Course Outline [PDF]
Course Content
Spatial interdependence is ubiquitous in the social sciences. The likelihood and outcomes of demonstrations, riots, coups, and revolutions in one country almost certainly depend in substantively crucial ways on such occurrences in other countries (e.g., through demonstration effects or snowballing). Election outcomes, candidate qualities or strategies in some contests surely depend on those in others, and individual legislators’ votes certainly depend on others votes or expected votes. In micro-behavioural research, the recently surging interest in contextual or network effects usually refers to the effects on each individual’s behaviour or opinion from sets of other individual’s opinions or behaviours; e.g., a respondent’s opinion on some policy likely depends on the opinions of her state, district, community, or social group. States entry decisions in wars, alliances, international organisations, e.g., heavily depend on how many and who else enters and how. Globalization, i.e., international economic integration, implies strategic and non-strategic interdependence in national-level macroeconomic policymaking. This course provides an introduction to spatial and spatiotemporal models for continuous and limited dependent variables with an emphasis on social science applications.
Course Objectives
The main objective of this course is to teach students how to incorporate the interdependence implied by most social scientific theories into their empirical analysis. Participants will learn inter alia how to 1) diagnose spatial patterns in their data, 2) estimate the structural parameters of spatial and spatiotemporal regression models, 3) calculate and present spatial and spatiotemporal effects, and 4) use spatial modelling to discriminate between the multiple sources of spatial correlation common exposure, interdependence, and selection and, when it exists, evaluate the nature of this interdependence (e.g., strategic free-riding behaviour, learning, coercion) among units of observation.
Course Prerequisites
Students should have a basic understanding of matrix algebra, probability theory, first-year calculus, and regression as well as some familiarity with a software package that can be used for spatial analysis (e.g., STATA, MATLAB, or R).
Reading
Anselin, L. 2006. Spatial Econometrics. In T.C. Mills and K. Patterson, eds., Palgrave Handbook of Econometrics: Volume 1, Econometrics Theory. Basingstoke: Palgrave Macmillan, pp. 901-941.
Franzese, R. and J. Hays. 2008. “Empirical Models of Spatial Interdependence” In Oxford Handbook of Political Methodology, Eds. Janet Box-Steffensmeier, Henry Brady, and David Collier, pp. 570-604, Oxford U.K.: Oxford University Press.
Beck, N., K. Gleditsch, and K. Beardsley. 2006. “Space is More than Geography: Using Spatial Econometrics in the Study of Political Economy.” International Studies Quarterly 50: 27-44.
Anselin, Luc. 1995. “Local Indicators of Spatial Association – LISA.” Geographical Analysis 27: 93-115.
Elhorst, J.P. 2001. “Dynamic Models in Space and Time.” Geographical Analysis 33:119-140.
Franzese, R.J and J.C. Hays. 2007. “Spatial-Econometric Models of Cross-Sectional Interdependence in Political Science Panel and Time-Series-Cross-Section Data.” Political Analysis 15(2): 140-164.