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.<BR>This text is intended for advanced undergraduate and graduate courses on time series analysis, quantitative research methods, or more advanced statistics, especially in the departments of Sociology, Psychology, Political Science, and Economics. It is also an excellent resource for researchers in the social sciences who are conducting time series analysis or econometric studies.