Time-series, or longitudinal, data are ubiquitous in the social sciences. Unfortunately, analysts often treat the time-series properties of their data as a nuisance rather than a substantively meaningful dynamic process to be modeled and interpreted. Time-Series Analysis for Social Sciences provides accessible, up-to-date instruction and examples of the core methods in time-series econometrics. Janet M. Box-Steffensmeier, John R. Freeman, Jon C. Pevehouse, and Matthew P. Hitt cover a wide range of topics including ARIMA models, time-series regression, unit-root diagnosis, vector autoregressive models, error-correction models, intervention models, fractional integration, ARCH models, structural breaks, and forecasting. This book is aimed at researchers and graduate students who have taken at least one course in multivariate regression. Examples are drawn from several areas of social science, including political behavior, elections, international conflict, criminology, and comparative political economy.
About the Author
Janet M. Box-Steffensmeier is Vernal Riffe Professor of Political Science and Professor of Sociology at Ohio State University (courtesy), where she is a University Distinguished Scholar and directs the Program in Statistics and Methodology (PRISM). Box-Steffensmeier served as president of the Midwest Political Science Association and the Political Methodology Society and as treasurer of the American Political Science Association. She has twice received the Gosnell Prize for the best work in political methodology, and she received the Emerging Scholar Award from the Elections, Public Opinion, and Voting Behavior Subsection of the American Political Science Association. She was an inaugural Fellow of the Society for Political Methodology. The Box-Steffensmeier Graduate Student Award, given annually by the Interuniversity Consortium for Political and Social Research (ICPSR), is named after her in recognition of her contributions in political methodology and her support of women in this field.
John R. Freeman is the John Black Johnston Distinguished Professor in the College of Liberal Arts at the University of Minnesota, and a Fellow of the American Academy of Arts and Sciences. Among his honors are the Morse-Alumni, All-University, and College of Liberal Arts Distinguished Teaching awards at the University of Minnesota. Freeman is the author of Democracy and Markets: The Politics of Mixed Economies, which won the International Studies Association's Quincy Wright Award, and the coauthor of Three Way Street: Strategic Reciprocity in World Politics. Freeman also edited three volumes of Political Analysis. He has (co)authored numerous research articles in academic journals. Many of Freeman's research projects have been supported by the National Science Foundation as well as by the Bank Austria Foundation and the Austrian Ministry of Science.
Jon C.W. Pevehouse is a Professor of Political Science at the University of Wisconsin. His work examines the relationship between domestic and international politics. Pevehouse is the author of Democracy from Above (Cambridge University Press, 2005) and While Dangers Gather (2007). He is the coauthor, with Joshua Goldstein, of International Relations, the leading textbook on international politics. He is the recipient of the Karl Deutch Award, given by the International Studies Association, and has received numerous teaching awards, including the Chancellor's Distinguished Teaching Award at the University of Wisconsin. Pevehouse is also the editor of the journal International Organization.
Matthew P. Hitt is a doctoral candidate in political science at the Ohio State University. His interests include judicial politics, legislative politics, interest groups, the presidency, and quantitative methodology. His research has been published in the American Political Science Review and Presidential Studies Quarterly.
Table of Contents
1. Modeling social dynamics; 2. Univariate time-series models; 3. Dynamic regression models; 4. Modeling the dynamics of social systems; 5. Univariate, nonstationary processes: tests and modeling; 6. Co-integration and error-correction models; 7. Selections on time-series analysis; 8. Concluding thoughts for the time-series analyst.