Germany, Cologne

Spatial Analysis and Spatial Econometrics

when 26 February 2018 - 2 March 2018
language English
duration 1 week
credits 3 ECTS
fee EUR 300

The 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 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.

Course leader

Jude C. Hays is Associate Professor of Political Science at the University of Pittsburgh.
Scott J. Cook is Assistant Professor of Political Science and Co-Director in the Program in Research Methods at Texas A&M University.

Target group

Participants will find the course useful if they
- use cross-sectional or time-series-cross-sectional (or longitudinal-network) data in their research,
- are interested in ensuring inferences are robust to possible spatial autocorrelation (i.e., space as nuisance),
- are interested in testing theories on how relationships among actors affect outcomes (i.e., space as substance).

Participants should have some familiarity with concepts from
- Time Series Analysis,
- Maximum Likelihood Estimation,
- Matrix Algebra.

Course aim

By the end of the course participants will
- test for spatial dependence in outcomes & residuals,
- estimate a variety of explicitly spatial models, including: SAR, SEM, SLX, and combinations thereof,
- select the appropriate model for their data & theory,
- calculate a present spatial and spatio-temporal effects.

Credits info

PhD students have the opportunity to receive European Credit Transfer System (ECTS) points thanks to our cooperation with the Cologne Graduate School in Management, Economics and Social Sciences of the University of Cologne. You will be charged an administration fee of EUR 50,00 (3 ECTS points).

Fee info

EUR 300: Student/PhD student rate.
EUR 450: Academic/non-profit/public sector rate.
The rates include the tuition fee, course materials, access to library and IT facilities, coffee/tea breaks and social activities.