Overview
- Provides an understanding of core parts of multivariate time series theory and models
- Presents a self-contained exposition with numerous examples and exercises
- Emphasizes weakly stationary processes and linear dynamic models
Part of the book series: Lecture Notes in Statistics (LNS, volume 224)
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About this book
This textbook provides a self-contained presentation of the theory and models of time series analysis. Putting an emphasis on weakly stationary processes and linear dynamic models, it describes the basic concepts, ideas, methods and results in a mathematically well-founded form and includes numerous examples and exercises. The first part presents the theory of weakly stationary processes in time and frequency domain, including prediction and filtering. The second part deals with multivariate AR, ARMA and state space models, which are the most important model classes for stationary processes, and addresses the structure of AR, ARMA and state space systems, Yule-Walker equations, factorization of rational spectral densities and Kalman filtering. Finally, there is a discussion of Granger causality, linear dynamic factor models and (G)ARCH models. The book provides a solid basis for advanced mathematics students and researchers in fields such as data-driven modeling, forecasting and filtering, which are important in statistics, control engineering, financial mathematics, econometrics and signal processing, among other subjects.
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Keywords
Table of contents (11 chapters)
Reviews
“The book is not too long, with roughly 200 pages, which is advantageous for the use in lectures. … it is also well suited for seminars following such lectures on more advanced topics in time series analysis. … the book fills a small but important gap in the literature, finding its place in the plethora of time series textbooks.” (Claudia Kirch, zbMATH 1532.62006, 2024)
“This lecture note is recommended as a textbook that is quite plainly written for graduate students and research workers who are interested in deeply understanding time series modeling.” (Yuzo Hosoya, Mathematical Reviews, October, 2023)
Authors and Affiliations
About the authors
Manfred Deistler is Emeritus Professor of Econometrics and System Theory at the Institute of Statistics and Mathematical Methods in Economics at the TU Wien, Vienna, Austria. His research interests include time series analysis, systems identification and econometrics. He is a Fellow of the Econometric Society, the IEEE, and the Journal of Econometrics.
Wolfgang Scherrer is a Professor of Econometrics and System Theory at the Institute of Statistics and Mathematical Methods in Economics at the TU Wien, Vienna, Austria. His research interests include time series analysis, econometrics, dynamic factor models and applications in the area of energy supply.
Bibliographic Information
Book Title: Time Series Models
Authors: Manfred Deistler, Wolfgang Scherrer
Series Title: Lecture Notes in Statistics
DOI: https://doi.org/10.1007/978-3-031-13213-1
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer Nature Switzerland AG 2022
Softcover ISBN: 978-3-031-13212-4Published: 22 October 2022
eBook ISBN: 978-3-031-13213-1Published: 21 October 2022
Series ISSN: 0930-0325
Series E-ISSN: 2197-7186
Edition Number: 1
Number of Pages: XIV, 201
Number of Illustrations: 3 b/w illustrations, 10 illustrations in colour
Topics: Statistical Theory and Methods, Probability Theory and Stochastic Processes, Econometrics, Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences, Signal, Image and Speech Processing