Introduction to Modern Time Series Analysis (Hardcover)

Gebhard Kirchgaessner

ISBN 10: 3642334350 / ISBN 13: 9783642334351

Verlag: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Berlin, 2012

Beschreibung

Hardcover. 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Bestandsnummer des Verkäufers 9783642334351

Bibliografische Details

• Titel: Introduction to Modern Time Series Analysis (Hardcover)
• Verlag: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Berlin
• Erscheinungsjahr: 2012
• Sprache: Englisch
• ISBN-10: 3642334350
• ISBN-13: 9783642334351
• Einband: Hardcover
• Auflage: 2. Auflage
• Zustand: new

Über diesen Titel

Inhaltsangabe

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.