Introduction to Modern Time Series Analysis
Language: English
Published by Springer-Verlag Berlin and Heidelberg GmbH and Co. KG, DE, 2014
Series: Book 12 of 190 - Springer Texts in Business and Economics
- Softcover
- New
Seller: Rarewaves.com USA, London, London, United KingdomRarewaves.com USA
AbeBooks seller since June 11, 2025
Condition: New
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Add to basketItem description from seller
This book presents modern developments in time series econometrics that are applied to macroeconomic and financial time series, bridging the gap between methods and realistic applications. It presents the most important approaches to the analysis of time series, which may be stationary or nonstationary. Modelling and forecasting univariate time series is the starting point. For multiple stationary time series, Granger causality tests and vector autogressive models are presented. As the modelling of nonstationary uni- or multivariate time series is most important for real applied work, unit root and cointegration analysis as well as vector error correction models are a central topic. Tools for analysing nonstationary data are then transferred to the panel framework. Modelling the (multivariate) volatility of financial time series with autogressive conditional heteroskedastic models is also treated.
Seller Inventory # LU-9783642440298
- Title
- Introduction to Modern Time Series Analysis
- Author
- Gebhard Kirchgässner, Jürgen Wolters, Uwe Hassler
- Publisher
- Springer-Verlag Berlin and Heidelberg GmbH and Co. KG, DE
- Publication year
- 2014
- Condition
- New
- Binding
- Paperback
- Language
- English
- ISBN 10
- 3642440290
- ISBN 13
- 9783642440298
- Edition
- Second Edition 2013.
- Dimensions
- 15.5 x 1.91 x 23.5 cm
- Series
- Book 12 of 190: Springer Texts in Business and Economics
This book presents modern developments in time series econometrics that are applied to macroeconomic and financial time series, bridging the gap between methods and realistic applications. It presents the most important approaches to the analysis of time series, which may be stationary or nonstationary. Modelling and forecasting univariate time series is the starting point. For multiple stationary time series, Granger causality tests and vector autogressive models are presented. As the modelling of nonstationary uni- or multivariate time series is most important for real applied work, unit root and cointegration analysis as well as vector error correction models are a central topic. Tools for analysing nonstationary data are then transferred to the panel framework. Modelling the (multivariate) volatility of financial time series with autogressive conditional heteroskedastic models is also treated.
"Synopsis" may belong to another edition of this title.
From the Back Cover
This book presents modern developments in time series econometrics that are applied to macroeconomic and financial time series, bridging the gap between methods and realistic applications. It presents the most important approaches to the analysis of time series, which may be stationary or nonstationary. Modelling and forecasting univariate time series is the starting point. For multiple stationary time series, Granger causality tests and vector autogressive models are presented. As the modelling of nonstationary uni- or multivariate time series is most important for real applied work, unit root and cointegration analysis as well as vector error correction models are a central topic. Tools for analysing nonstationary data are then transferred to the panel framework. Modelling the (multivariate) volatility of financial time series with autogressive conditional heteroskedastic models is also treated.
"About the title" may belong to another edition of this title.
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London, London, United Kingdom
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