Titlebook: Elements of Multivariate Time Series Analysis; Gregory C. Reinsel Textbook 19931st edition Springer-Verlag New York, Inc. 1993 Likelihood.

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Theodor Lambrianidis D.D.S., Ph.D.rate special structure in their parameterization, in particular, the nested reduced-rank models, which attempt to cope with the problem of the high dimensionality of the parameters in the vector models. Model specification methods, based on partial canonical correlation analysis, and parameter estim

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CUMB

Vector Time Series and Model Representations,everal related time series processes are observed simultaneously over time, instead of observing just a single series as is the case in univariate time series analysis. Multivariate time series processes are of considerable interest in a variety of fields such as engineering, the physical sciences,

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Vector ARMA Time Series Models and Forecasting,nvertibility aspects of vector ARMA processes are considered. The covariance matrix structure of vector ARMA processes is considered, in general as well as for special cases such as first-order MA, AR, and ARMA models. In addition, consideration of parameter identifiability of mixed ARMA model repre

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Initial Model Building and Least Squares Estimation for Vector AR Models,ced and discussed. Least squares estimation for vector AR models and associated tests of hypothesis for the order of the AR model are emphasized. Properties of least squares estimates for vector AR models are discussed. Additional methods for initial specification and selection of an appropriate ARM

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Maximum Likelihood Estimation and Model Checking for Vector ARMA Models,ties are examined. For conditional maximum likelihood, explicit iterative computation of the ML estimator in the form of generalized least squares estimation is presented, while for the exact likelihood method, two different approaches to computation of the exact likelihood function are developed. M

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Reduced-Rank and Nonstationary Co-Integrated Models,rate special structure in their parameterization, in particular, the nested reduced-rank models, which attempt to cope with the problem of the high dimensionality of the parameters in the vector models. Model specification methods, based on partial canonical correlation analysis, and parameter estim

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