Multiple Time Series Models

Patrick T. Brandt is an Assistant Professor of Political Science in the School of Social Science at the University of Texas at Dallas. He has published in the American Journal of Political Science and Political Analysis. He teaches courses in social science research methods and social science statistics. His current research focuses on the development and application of time series models to the study of political institutions, political economy, and international relations. He received an A.B. (1990) in Government from the College of William and Mary, an M.S. (1997) in Mathematical Methods in the Social Sciences from Northwestern University, and a Ph.D. (2001) in Political Science from Indiana University. Before joining the faculty at the University of Texas at Dallas, he held positions at the University of North Texas, Indiana University, and as a fellow at the Harvard-MIT Data Center. John T. Williams was Professor and Chair of the Department of Political Science at University of California, Riverside. He taught time series analysis at the Inter-university Consortium for Political and Social Research Summer Training Program for over ten years. His work uses statistical methods in the study of political economy and public policy. He co-authored two books: Compound Dilemmas: Democracy, Collective Action, and Superpower Rivalry (University of Michigan Press, 2001) and Public Policy Analysis: A Political Economy Approach (Houghton Mifflin, 2000). He published over twenty journal articles and book chapters on a wide range of topics, ranging from macroeconomic policy to defense spending to forest resource management. He was a leader in the application of new methods of statistical analysis to political science, especially the use of vector autoregression (VAR), Bayesian, and event count time series models. He received a B.A. (1979), an M.A. (1981) from North Texas State University, and a Ph.D. (1987) from the University of Minnesota. Before moving to Riverside in 2001, he held academic positions at the University of Illinois Chicago (1985-1990) and at Indiana University, Bloomington (1990-2001).

List of Figures List of Tables Series Editor?s Introduction Preface 1. Introduction to Multiple Time Series Models 1.1 Simultaneous Equation Approach 1.2 ARIMA Approach 1.3 Error Correction or LSE Approach 1.4 Vector Autoregression Approach 1.5 Comparison and Summary 2. Basic Vector Autoregression Models 2.1 Dynamic Structural Equation Models 2.2 Reduced Form Vector Autoregressions 2.3 Relationship of a Dynamic Structural Equation Model to a Vector Autoregression Model 2.4 Working With This Model 2.5 Specification and Analysis of VAR Models 2.6 Other Specification Issues 2.7 Unit Roots and Error Correction in VARs 2.8 Criticisms of VAR 3. Examples of VAR Analyses 3.1 Public Mood and Macropartisanship 3.2 Effective Corporate Tax Rates 3.3 Conclusion Appendix: Software for Multiple Time Series Models Notes References Index About the Authors

"This book amazingly introduces multiple time series on varied levels to help the reader to understand their assumptions, their four approaches, how to build theories to accompany their modeling, and how to interpret their results. This book would be quite an initiation, sweet and succinct, in advanced undergraduate and graduate courses on time series. In addition, it is a useful and reliable resource . . . this book also makes a fun reading!" -- Ruth Chao * Contemporary Psychology: APA Review *