Introduction to Modern Time Series Analysis

This book presents modern methods of time series econometrics and their applications to macroeconomics and finance. It includes numerous examples and analyses based on real economic data.

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.

Autorentext
Gebhard Kirchgässner ist Professor für Volkswirtschaftslehre und Ökonometrie an der Universität St. Gallen.

Inhalt
Introduction and Basics.- Univariate Stationary Processes.- Granger Causality.- Vector Autoregressive Processes.- Nonstationary Processes.- Cointegration.- Nonstationary Panel Data.- Autoregressive Conditional Heteroscedasticity.