Political Science 8125: Dynamic Analysis

Time Series Modeling in Politics, Parts I & II

Winter Semester 2006
11:00-1:00 CST, Fridays
U.of MN: Electronic Classroom, Rarig Hall
OSU: Lord Hall
U. of WI: The Pyle Center
U. of IL: Room 103, 508 S. 6th Street

Jan Box-Steffensmeier
2010 Derby Hall
Ohio State University
(614) 292-9642
Steffensmeier.2@osu.edu

John R. Freeman
1414 Social Sciences
University of Minnesota
(612) 624-4144
freeman@polisci.umn.edu

Jon Pevehouse
416 North Hall
University of Wisconsin
(608) 262-4839
pevehous@polisci.wisc.edu

Class Resources

  • Syllabus for 2006 here.
  • Threaded discussion list here.
  • Class notes here.
  • Class readings here (password protected).
  • Class assignments here.
  • Meet the Faculty and Students 2006, 2004.
  • View Walter Enders' talk at the Mershon here.

This course considers statistical techniques to evaluate social processes occurring through time. The course introduces students to time series methods and to the applications of these methods in political science. We begin by discussing social problems that are inherently dynamic in nature and also how time series are measured. We then review the calculus of finite differences and other estimation techniques. We move next to study stationary ARMA models. In the following section of the course, we examine a number of important topics in time series analysis including "reduced form" methods (granger causality and vector autogression), unit root tests, near-integration, fractional integration, cointegration, and error correction models. Time series regression is also discussed (including pooling cross-sectional and time series data). We learn not only how to construct these models but also how to use them in social science analyses.

We expect students to have a firm grounding in probability and regression analysis and to bring to the course some interesting questions about the dynamics of political processes. The emphasis throughout the course will be on application, rather than on statistical theory. However, the focus of most lectures will be statistical theory. Homework will revolve as much as possible around the time series you are interested in understanding. To that end, students will need to gather time serial data for analysis during the first week of class (these data need not be used throughout the term, though that would make your life easier). The length of the series should be at least 40 time points; longer series are better than shorter ones.

This is the first part of a fourteen-week seminar team-taught by Professors John Freeman, Janet Box-Steffensmeier, and Jon Pevehouse. Students are strongly encouraged to take both parts of the course.





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