Beschreibung
Now available in paperback, this book introduces basic concepts and methods useful in the analysis and modeling of multivariate time series data. It concentrates on the time-domain analysis of multivariate time series, and assumes univariate time series analysis, while covering basic topics such as stationary processes and their covariance matrix structure, vector AR, MA, and ARMA models, forecasting, least squares and maximum likelihood estimation for ARMA models, associated likelihood ratio testing procedures.
Inhaltsverzeichnis
Vector Time Series and Model Representations; Vector ARMA Time Series Models and Forecasting; Canonical Structure of Vector ARMA Models; Initial Model Building and Least Squares Estimation for Vector AR Models; Maximum Likelihood Estimation and Model Checking for Vector ARMA Models; Reduced-Rank and Nonstationary Cointegrated Models; State-Space Models, Kalman Filtering, and Related Topics; Linear Models with Exogenous Variables; Appendix: Time series data sets. Exercises and Problems; References; Subject Index; Author Index.
Autorenporträt
InhaltsangabeVector Time Series and Model Representations; Vector ARMA Time Series Models and Forecasting; Canonical Structure of Vector ARMA Models; Initial Model Building and Least Squares Estimation for Vector AR Models; Maximum Likelihood Estimation and Model Checking for Vector ARMA Models; Reduced-Rank and Nonstationary Cointegrated Models; State-Space Models, Kalman Filtering, and Related Topics; Linear Models with Exogenous Variables; Appendix: Time series data sets. Exercises and Problems; References; Subject Index; Author Index.
