Elements of Multivariate Time Series Analysis (Springer Series in Statistics)

Reinsel, Gregory C.

ISBN 10: 0387949186 / ISBN 13: 9780387949185

Editore: Springer, 1997

Riguardo questo articolo

Ex-library copy, with the usual markings/stickers/stamping present. Otherwise book & it's text block show minimal shelf & handling wear, crisp and clean. Text/pictures are unmarked, binding intact and firm! A good reading copy.

Codice articolo mon0004207281

Dati bibliografici

• Titolo: Elements of Multivariate Time Series Analysis (Springer Series in Statistics)
• Editore: Springer
• Data di pubblicazione: 1997
• Lingua: Inglese
• ISBN 10: 0387949186
• ISBN 13: 9780387949185
• Rilegatura: hardcover
• Edizione: seconda edizione
• Condizione: Good

Descrizione articolo

Riassunto

Elements of Multivariate Time Series Analysis, Second Edition introduces the basic concepts and methods that are useful in the analysis and modeling of multivariate time series data that may arise in business and economics, engineering, geophysical sciences, and other fields. The book concentrates on the time-domain analysis of multivariate time series, and assumes a background in univariate time series analysis. It covers 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, and other model specification methods useful for model building and model checking. In this revised edition, additional topics have been added and parts of the first edition have been expanded. The most notable addition is a new chapter that discusses topics that arise when exogenous variables are involved in model structures, generally through consideration of the ARMAX models. The book also includes exercise sets and multivariate time series data sets. In addition to serving as a textbook, this book will also be useful to researchers and graduate students in the areas of statistics, econometrics, business, and engineering.

Contenuti

Contents: 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 co- integrated models; State-space models, Kalman filtering, and related topics; Linear models with exogenous variables; Appendix: Time series data sets.