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Time Series Econometrics

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Publisher:

Springer

Pages: 409
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Recommend to library

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Hardcover - 9783319328614

21 June 2016

$107.99

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Ebook - 9783319328621

14 June 2016

$84.99

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This text presents modern developments in time series analysis and focuses on their application to economic problems. The book first introduces the fundamental concept of a stationary time series and the basic properties of...

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This text presents modern developments in time series analysis and focuses on their application to economic problems. The book first introduces the fundamental concept of a stationary time series and the basic properties of covariance, investigating the structure and estimation of autoregressive-moving average (ARMA) models and their relations to the covariance structure. The book then moves on to non-stationary time series, highlighting its consequences for modeling and forecasting and presenting standard statistical tests and regressions. Next, the text discusses volatility models and their applications in the analysis of financial market data, focusing on generalized autoregressive conditional heteroskedastic (GARCH) models. The second part of the text  devoted to multivariate processes, such as vector autoregressive (VAR) models and structural vector autoregressive (SVAR) models, which have become the main tools in empirical macroeconomics. The text concludes with a discussion of co-integrated models and the Kalman Filter, which is being used with increasing frequency. Mathematically rigorous, yet application-oriented, this self-contained text will help students develop a deeper understanding of theory and better command of the models that are vital to the field.  Assuming a basic knowledge of statistics and/or econometrics, this text is best suited for advanced undergraduate and beginning graduate students. 

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Analyzes modern developments in time series analysis and their application to economic problems

Introduces the fundamental concept of a stationary time series and the basic properties of covariance

Helps students develop a deeper understanding of theory and better command of the models that are vital to the field  


1. Introduction
2. ARMA models
3. Forecasting stationary processes
4. Estimation of Mean and Autocovariance Function
5.Estimation of ARMA Models
6. Spectral Analysis and Linear Filters
7. Integrated Processes
8. Models of Volatility
9. Multivariate Time series
10. Estimation of Covariance Function
11. VARMA Processes
12. Estimation of VAR Models
13. Forecasting with VAR Models
14. Interpretation of VAR Models
15. Co-integration
16. The Kalman Filter
17. Appendices.
“The present monograph is a practical and comprehensive introduction to an area that lies at the core of econometrics. … It requires minimal prerequisites, and is almost surely accessible to senior undergraduate or beginning graduate students, and certainly to independent researchers … . I find this book to be a valuable addition to the monographic literature on time series.” (Giuseppe Castellacci, Mathematical Reviews, October, 2017)
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Prof. Klaus Neusser

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Prof. Klaus Neusser

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