2I Panel Data Analysis for Comparative Research
Chris Adolph, University of Washington–Seattle
23 July - 3 August (two week course / 35 hrs)
Detailed Course Outline [PDF]
THIS COURSE IS NOW FULLY BOOKED AND WE ARE OPERATING A WAITING LIST
Course Content
After a review of the theory and estimation of linear regression and maximum likelihood, we will cover the following topics: modelling time series dynamics using ARIMA models, lagged dependent variables, and distributed lags; cointegration and error correction models; modelling cross-sectional variation using fixed and random effects; coping with panel heteroskedasticity, and presentation and interpretation of TS and TSCS models.
Course Objectives
This course provides a survey of regression models for time series (TS) and time series cross-section (TSCS) data, with an emphasis on modelling dynamics and panel structures. For many political science subfields, including political economy, international relations, and comparative politics, data of this type are ubiquitous, and training in TSCS analysis essential for quantitative research. Participants will gain an introductory understanding of the theory behind TSCS models and a working understanding of how to estimate, select, and interpret these models.
Course Prerequisites
Students should enter the course with a solid understanding of first year statistics as taught in a standard political science doctoral program, an interest in data with either a time series or time series cross-sectional (panel) data structure, and either exposure to, or willingness to try, the R statistical package.
R
In-class code examples will use the R statistical package, which is powerful, free, open source, widely used, and rapidly becoming the standard for quantitative work in political science and other fields. You can obtain R at r-project. Throughout the course, I will provide example code in R, and can only promise detailed homework help for the R package.
Required Reading
Selections from the following books and articles will be provided as part of the course materials. Students seeking to get a headstart should focus on the assigned readings for the first week; those without a background in R should especially concentrate on Zuur.
Nathaniel Beck & Jonathan Katz. 1995. What to Do (And Not to Do) With Time Series Cross-Section Data." American Political Science Review.
Paul S.P. Cowpertwait & Andrew V. Metcalfe. 2009. Introductory Time Series with R. Springer-Verlag.
Gary King. 1989. Unifying political methodology. University of Michigan Press.
Gary King, Michael Tomz, and Jason Wittenberg. 2000. Making the Most of Statistical Analyses." American Journal of Political Science
Bernhard Pfaff. 2008. Analysis of Integrated Series with R. Springer-Verlag.
Jeffrey M. Wooldridge. 2002. Econometric Analysis of Cross-Sectional and Panel Data. MIT
Alain F. Zuur, Elena N. Ieno, and Erik H.W.G. Meesters. 2009. A Beginners Guide to R. Springer-Verlag.