Introduction to Modern Time Series Analysis
By: by Gebhard Kirchgässner, Jürgen Wolters, Uwe Hassler
Contributors: Wolters, Jürgen; Hassler, Uwe; SpringerLink (Online service)
Edition: 2nd ed. 2013.
Series: Springer Texts in Business and Economics
Publisher: Springer Berlin Heidelberg; Imprint: Springer
Published: 2013
Place of publication: Berlin, Heidelberg
Language: eng
Format: [electronic resource] /
Number of pages: 319
ISBN-13: 9783642334368
Subjects: Macroeconomics; Economics/Management Science; Statistics; Finance; Economics; Financial Economics; Macroeconomics/Monetary Economics; Game Theory, Economics, Social and Behav. Sciences; Mathematics; Statistics for Business/Economics/Mathematical Finance/Insurance; Econometrics
Description: <p>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.</p><p> </p>
Table of contents: title: Introduction and Basics; title: Univariate Stationary Processes; title: Granger Causality; title: Vector Autoregressive Processes; title: Nonstationary Processes; title: Cointegration; title: Nonstationary Panel Data; title: Autoregressive Conditional Heteroscedasticity.