Multiple Time Series Models introduces researchers and students to the different approaches to modeling multivariate time series data including simultaneous equations, ARIMA, error correction models, and vector autoregression. Authors Patrick T. Brandt and John T. Williams focus on vector autoregression (VAR) models as a generalization of these other approaches and discuss specification, estimation, and inference using these models.
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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.
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