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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.
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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.
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- VerlagSpringer
- Erscheinungsdatum2012
- ISBN 10 3642334350
- ISBN 13 9783642334351
- EinbandTapa dura
- SpracheEnglisch
- Anzahl der Seiten332
- Kontakt zum HerstellerNicht verfügbar
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Introduction to Modern Time Series Analysis (Springer Texts in Business and Economics)
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Introduction to Modern Time Series Analysis (Springer Texts in Business and Economics)
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Introduction to Modern Time Series Analysis
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Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents modern methods of time series econometrics and their applications to macroeconomics and financeWith numerous examples and analyses based on real economic dataHelps to acquire a rigorous understanding of the methods and to develop e. Bestandsnummer des Verkäufers 5057267
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Introduction to Modern Time Series Analysis
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Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Bestandsnummer des Verkäufers 9783642334351
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Introduction to Modern Time Series Analysis
Verlag:
Springer Berlin Heidelberg Okt 2012, 2012
ISBN 10: 3642334350
ISBN 13: 9783642334351
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Buch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 332 pp. Englisch. Bestandsnummer des Verkäufers 9783642334351
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Introduction to Modern Time Series Analysis
ISBN 10: 3642334350
ISBN 13: 9783642334351
Neu
Hardcover
Anbieter: buchversandmimpf2000, Emtmannsberg, BAYE, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Buch. Zustand: Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 332 pp. Englisch. Bestandsnummer des Verkäufers 9783642334351
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