| Elements of Multivariate Time Series Analysis Softcover Repri Edition Contributor(s): Reinsel, Gregory C. (Author) |
|
 |
ISBN: 0387406190 ISBN-13: 9780387406190 Publisher: Springer OUR PRICE: $52.24 Product Type: Paperback - Other Formats Published: October 2003 Annotation: Now available in paperback. 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. |
| Additional Information |
| BISAC Categories: - Mathematics | Probability & Statistics - Multivariate Analysis |
| Dewey: 519.55 |
| Series: Springer Statistics |
| Physical Information: 0.7" H x 6.14" W x 9.18" (1.16 lbs) 358 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: In this revised edition, some additional topics have been added to the original version, and certain existing materials have been expanded, in an attempt to pro- vide a more complete coverage of the topics of time-domain multivariate time series modeling and analysis. The most notable new addition is an entirely new chapter that gives accounts on various topics that arise when exogenous vari- ables are involved in the model structures, generally through consideration of the so-called ARMAX models; this includes some consideration of multivariate linear regression models with ARMA noise structure for the errors. Some other new material consists of the inclusion of a new Section 2. 6, which introduces state-space forms of the vector ARMA model at an earlier stage so that readers have some exposure to this important concept much sooner than in the first edi- tion; a new Appendix A2, which provides explicit details concerning the rela- tionships between the autoregressive (AR) and moving average (MA) parameter coefficient matrices and the corresponding covariance matrices of a vector ARMA process, with descriptions of methods to compute the covariance matrices in terms of the AR and MA parameter matrices; a new Section 5. |