- Išparduota
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- Autorius: Gebhard Kirchgässner, Jürgen Wolters
- Leidėjas: Springer
- Metai: 2008
- Puslapiai: 288
- ISBN-10: 3540687351
- ISBN-13: 9783540687351
- Formatas: 15 x 23 cm, minkšti viršeliai
- Kalba: Anglų
Introduction to Modern Time Series Analysis (el. knyga) (skaityta knyga) | knygos.lt
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Aprašymas
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.
- Kaina: 26,99 €
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- Autorius: Gebhard Kirchgässner, Jürgen Wolters
- Leidėjas: Springer
- Metai: 2008
- Puslapiai: 288
- ISBN-10: 3540687351
- ISBN-13: 9783540687351
- Formatas: 15 x 23 cm, minkšti viršeliai
- Kalba: Anglų
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.
Atsiliepimai