Forecasting—the art and science of predicting future outcomes—has become a crucial skill in business and economic analysis. This volume introduces the reader to the tools, methods, and techniques of forecasting, specifically as they apply to financial and investing decisions. With an emphasis on "ea
[Springer Texts in Business and Economics] Introduction to Modern Time Series Analysis || Granger Causality
✍ Scribed by Kirchgässner, Gebhard; Wolters, Jürgen; Hassler, Uwe
- Book ID
- 120469503
- Publisher
- Springer Berlin Heidelberg
- Year
- 2012
- Tongue
- German
- Weight
- 737 KB
- Edition
- 2
- Category
- Article
- ISBN
- 3642334369
No coin nor oath required. For personal study only.
✦ 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.
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