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Résumé :
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Basic concepts and methods useful in the analysis and modeling of multivariate time series data. The volume covers basic topics such as stationary processes and their covariance matrix structure, vector AR, MA, and ARMA models, forecasting, least squares and maximum likehood estimation for ARMA models, associated likelihood ratio testing procedures, and other model specification methods useful for building and checking models. In addition, the book presents more detailed topics, including structural of Kronecker indices and echelon form models, scalar component models, reduced rank structure, canonical correlation analyses for vector time series, multivariate nonstationary unit-root models and cointegration structure, and state-space models and Kalman filtering techniques. This last edition presents a new chapter discussing topics that arise when exogenous variables are involved in models structures, generally through consideration of the ARMAX models.
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