Introduction to Modern Time Series Analysis

Gebhard Kirchgässner

ISBN 10: 3642440290 / ISBN 13: 9783642440298

Published by Springer Berlin Heidelberg Nov 2014, 2014

About this Item

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.

Seller Inventory # 9783642440298

Bibliographic Details

• Title: Introduction to Modern Time Series Analysis
• Publisher: Springer Berlin Heidelberg Nov 2014
• Publication Date: 2014
• Language: English
• ISBN 10: 3642440290
• ISBN 13: 9783642440298
• Binding: Taschenbuch
• Edition: 2nd Edition
• Condition: Neu
• Dimensions: 235W x 155H x 19D millimeters 235 millimeters width by 155 millimeters height by 19 millimeters depth
• Weight: 505 grams

About this title

Synopsis

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.