A Practical Guide to Time Series Analysis

Suzanna Linn is Distinguished Professor of Political Science in the Department of Political Science at Penn State University. She is a Fellow of the Society for Political Methodology and its immediate past president. She serves as associate editor of the Society's journal Political Analysis. Linn has served on the APSA and Midwest Political Science Association Councils. Her innovative research in political methodology and behavior appears in leading journals. Her book The Decline of the Death Penalty (Cambridge, 2008) won the Kammerer Award for best book on US national policy. Matthew J. Lebo is Professor of Political Science and Director of the Centre for Computational and Quantitative Social Science at Western University. His expertise is in political methodology and American politics. He has been a post-doctoral fellow at Harvard, Academic Visitor at Oxford, and a Visiting Professor at the University of Toronto and McGill University. Clayton Webb is an associate professor of Political Science at the University of Kansas. He received his Ph.D. from Texas A&M University. His areas of expertise are Political Methodology and International Relations. His work has been featured in Political Analysis, the American Journal of Political Science, and International Studies Quarterly.

'They don't say so, but this impressive, comprehensive volume is the result of the authors' lifetimes of careful study of social science time series methods. Many of us have been privileged to hear presentations on their research leading to this milestone, and now everyone can benefit by reading this book and understanding the big picture they have been working towards.' Gary King, Albert J Weatherhead III University Professor, and Director of the Institute for Quantitative Social Science, Harvard University

• 1. Introduction; Part I. Preliminary Questions: 2. Univariate time series models; 3. Testing for unit roots and stationarity; 4. The exogeneity question; Part II. Single-Equation Regression Models: 5. Regression models for stationary time series; 6. Regression models for unit root time series; 7. Regression models for time series with uncertain properties; Part III. Multiple-Equation Regression Models: 8. Vector autoregression for stationary time series; 9. Vector autoregression for unit root time series; 10. Time series analysis with transparency.