Finite Order Vector Autoregressive Processes -- Stable Vector Autoregressive Processes -- Estimation of Vector Autoregressive Processes -- VAR Order Selection and Checking the Model Adequacy -- VAR Processes with Parameter Constraints -- Cointegrated Processes -- Vector Error Correction Models -- Estimation of Vector Error Correction Models -- Specification of VECMs -- Structural and Conditional Models -- Structural VARs and VECMs -- Systems of Dynamic Simultaneous Equations -- Infinite Order Vector Autoregressive Processes -- Vector Autoregressive Moving Average Processes -- Estimation of VARMA Models -- Specification and Checking the Adequacy of VARMA Models -- Cointegrated VARMA Processes -- Fitting Finite Order VAR Models to Infinite Order Processes -- Time Series Topics -- Multivariate ARCH and GARCH Models -- Periodic VAR Processes and Intervention Models -- State Space Models.

This reference work and graduate level textbook considers a wide range of models and methods for analyzing and forecasting multiple time series. The models covered include vector autoregressive, cointegrated, vector autoregressive moving average, multivariate ARCH and periodic processes as well as dynamic simultaneous equations and state space models. Least squares, maximum likelihood, and Bayesian methods are considered for estimating these models. Different procedures for model selection and model specification are treated and a wide range of tests and criteria for model checking are introduced. Causality analysis, impulse response analysis and innovation accounting are presented as tools for structural analysis. The book is accessible to graduate students in business and economics. In addition, multiple time series courses in other fields such as statistics and engineering may be based on it. Applied researchers involved in analyzing multiple time series may benefit from the book as it provides the background and tools for their tasks. It bridges the gap to the difficult technical literature on the topic.