Christopher Adolph is Associate Professor of Political Science and Adjunct Associate Professor of Statistics at the University of Washington, Seattle, where he is also a core faculty member of the Center for Statistics and the Social Sciences. His book, Bankers, Bureaucrats, and Central Bank Politics: The Myth of Neutrality (Cambridge University Press), won the International Political Science Association’s Charles H. Levine Prize for best contribution in comparative public policy and administration. He is a former Robert Wood Johnson Scholar in Health Policy Research and won the American Political Science Association’s Mancur Olson Award for best dissertation in political economy. His research on comparative political economy and quantitative methods has appeared in American Political Science Review, Political Analysis, Social Science & Medicine, and other academic journals.
This course provides a survey of regression models for time series (TS) and time series cross-section (TSCS) data, with an emphasis on modeling dynamics and panel structures. After a review of the theory and estimation of linear regression and maximum likelihood, we will cover the following topics: modeling time series dynamics using ARIMA models, lagged dependent variables, and distributed lags; cointegration and error correction models; modeling cross-sectional variation using fixed and random effects; coping with panel heteroskedasticity, and presentation and interpretation of TS and TSCS models. Time permitting, we will cover advanced topics based on student interest, which in past years have included TSCS models for binary, ordered, or categorical data, multiple imputation for missing data in panel datasets, and linkages between panel data and hierarchical linear models.
For many political science subfields, including political economy, international relations, and comparative politics, panel data 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. Emphasis is placed on the development of both conceptual understanding and the ability to apply tools learned in the class using a variety of packages available for the R statistical language.
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. Specifically, students should be familiar with basic data structures and computing, elementary matrix algebra, and the basic theory and application of the linear regression model.
All Readings will be provided; however, students unfamiliar with R will benefit from reading this book ahead of the class:
Alain F. Zuur, Elena N. Ieno, and Erik H.W.G. Meesters. 2009. A Beginner’s Guide to R. Springer-Verlag.
Background knowledge required
OLS = moderate
R = elementary (or willingness to quickly learn)