Scott Cook is an Assistant Professor in the Department of Political Science at Texas A&M University. Previously, he earned his Ph.D. at the University of Pittsburgh (2014) where his doctoral work received the John T. Williams Dissertation Prize from the Society for Political Methodology. Research interests include spatial econometrics, discrete-choice methods, maximum likelihood, and measurement error, focusing on applications in international relations and comparative politics. His research has appeared in Political Analysis, Political Science Research and Methods, the Journal of Peace Research, and he is under contract with Cambridge University Press for a book on spatial methods (Empirical Analysis of Spatial Interdependence).
This course focuses on detecting, estimating, and analyzing models of spatially dependent data. Spatial interdependence – that the actions, outcomes, behaviors of some units are affected by those of other units – is ubiquitous throughout the social sciences. It includes not simply geographic space, but, more interestingly, any means by which we can conceive of units being linked (e.g., cultural ties, political affiliations, economic relationships). As such, many of the most interesting phenomena in political science have a theoretically meaningfully spatial component: contextual or network effects on individual voting behaviors and opinions; strategic decision making amongst two or more actors (e.g., countries in a conflict, parties in an election, votes in a legislature); the diffusion of demonstrations, riots, coups, and wars, etc… This course demonstrates how to effectively model such dependence using spatial and spatiotemporal econometric models.
This course operates from the belief that spatial dependence is theoretically meaningful and substantively interesting. Therefore, we will emphasize the importance of understanding the nature of the spatial dependence within our data and what it suggests for our analysis. Specific attention will be paid to detection and specification, highlighting the importance of discriminating between competing spatial theories.
In addition to these issues of specification, the course focuses on the practical application of these models. Given that one of the biggest impediments for researchers in undertaking spatial analysis is simply arranging their data appropriately, we begin the labs addressing basic data management and work our way through to the estimation of parameters and effects. Throughout the labs students are provided materials which will enable them to fully undertake an applied spatial project.
All necessary background material will be covered (in brief), however, students possessing a basic understanding of matrix algebra, probability theory, and maximum likelihood, will get the most from the course.
Representative Background Reading
Anselin, L., Florax, R., & Rey, S. J. (Eds.). (2004). Advances in spatial econometrics: methodology, tools and applications. Springer.
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.
Elhorst, J. P. 2014. Spatial Econometrics. Springer Berlin Heidelberg.
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.
Franzese, R.J, and J.C. 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.
LeSage, J., & Pace, R. K. 2009. Introduction to Spatial Econometrics. CRC press.
Ward, M.D. and K.S. Gleditsch. 2008. Spatial Regression Models. Sage.
Background knowledge required
Maximum Likelihood = m
Stata = m
R = m
e = elementary, m = moderate, s = strong