Introduction to Modern Time Series Analysis

Gebhard Kirchgässner

ISBN 10: 3642440290 / ISBN 13: 9783642440298

Verlag: Springer Berlin Heidelberg, Springer Berlin Heidelberg Nov 2014, 2014

Beschreibung

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 9783642440298

Bibliografische Details

• Titel: Introduction to Modern Time Series Analysis
• Verlag: Springer Berlin Heidelberg, Springer Berlin Heidelberg Nov 2014
• Erscheinungsjahr: 2014
• Sprache: Englisch
• ISBN-10: 3642440290
• ISBN-13: 9783642440298
• Einband: Taschenbuch
• Auflage: 2. Auflage
• Zustand: Neu
• Abmessungen: 235B x 155 H x 19 T Millimeter Breite: 235 mm, Höhe: 155 mm, Tiefe: 19 mm
• Gewicht: 505 Gramm

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.