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Introduction to Modern Time Series Analysis [electronic resource] / by Gebhard Kirchgässner, Jürgen Wolters, Uwe Hassler.

By: Kirchgässner, Gebhard [author.].
Contributor(s): Wolters, Jürgen [author.] | Hassler, Uwe [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Springer Texts in Business and Economics: Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013Edition: 2nd ed. 2013.Description: XII, 319 p. 42 illus. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783642334368.Subject(s): Economics | Mathematics | Economics -- Statistics | Econometrics | Macroeconomics | Finance | Economics/Management Science | Econometrics | Statistics for Business/Economics/Mathematical Finance/Insurance | Game Theory, Economics, Social and Behav. Sciences | Macroeconomics/Monetary Economics | Financial EconomicsDDC classification: 330.015195 Online resources: Click here to access online
Contents:
Introduction and Basics -- Univariate Stationary Processes -- Granger Causality -- Vector Autoregressive Processes -- Nonstationary Processes -- Cointegration -- Nonstationary Panel Data -- Autoregressive Conditional Heteroscedasticity.
In: Springer eBooksSummary: 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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Introduction and Basics -- Univariate Stationary Processes -- Granger Causality -- Vector Autoregressive Processes -- Nonstationary Processes -- Cointegration -- Nonstationary Panel Data -- Autoregressive Conditional Heteroscedasticity.

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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