Introduction to Modern Time Series Analysis, 2nd edition


Gebhard Kirchgässner, Jürgen Wolters, "Introduction to Modern Time Series Analysis, 2nd edition"  
2013 | ISBN-10: 3642334350 | 331 pages | PDF | 3,9 MB   

Series: Springer Texts in Business and Economics        
Kirchgässner, Gebhard, Wolters, Jürgen, Hassler, Uwe
2nd ed. 2013, XII, 319 p. 42 illus.
ISBN 978-3-642-33436-8

http://www.springer.com/economics/econometrics/book/978-3-642-33435-1
                                                            
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.  

• Presents modern methods of time series econometrics and their applications to macroeconomics and finance
• With numerous examples and analyses based on real economic data
• Helps to acquire a rigorous understanding of the methods and to develop empirical skills
  

       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.

Content Level » Research
Keywords »  Cointegration      Granger Causality   Time Series Analysis    Unit Roots   Vector Autogressive Models   Volatility
Related subjects » Applications   Business, Economics & Finance     Econometrics / Statistics    Financial Economics                    Macroeconomics / Monetary Economics / Growth