Time Series Models
Springer International Publishing (Verlag)
978-3-031-13212-4 (ISBN)
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.
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.
Preface.- 1 Time Series and Stationary Processes.- 2 Prediction.- 3 Spectral Representation.- 4 Filter.- 5 Autoregressive Processes.- 6 ARMA Systems and ARMA Processes.- 7 State-Space Systems.- 8 Models with Exogenous Variables.- 9 Granger Causality.- 10 Dynamic Factor Models.- 10 ARCH and GARCH Models.- Index.
"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)
“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)
| Erscheinungsdatum | 23.10.2022 |
|---|---|
| Reihe/Serie | Lecture Notes in Statistics |
| Zusatzinfo | XIV, 201 p. 13 illus., 10 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 335 g |
| Themenwelt | Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik |
| Wirtschaft ► Volkswirtschaftslehre ► Ökonometrie | |
| Schlagworte | AR and ARMA Processes • ARCH and GARCH Models • factor models • Forecasting and Filtering • Granger Causality • linear dynamical systems • multivariate time series • State Space Systems • Time and Frequency Domain • Time Series Analysis • weakly stationary processes |
| ISBN-10 | 3-031-13212-2 / 3031132122 |
| ISBN-13 | 978-3-031-13212-4 / 9783031132124 |
| Zustand | Neuware |
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