Secure CheckoutPersonal information is secured with SSL technology.
Free ShippingFree global shipping
No minimum order.
Written for those who need an introduction, Applied Time Series Analysis reviews applications of the popular econometric analysis technique across disciplines. Carefully balancing accessibility with rigor, it spans economics, finance, economic history, climatology, meteorology, and public health. Terence Mills provides a practical, step-by-step approach that emphasizes core theories and results without becoming bogged down by excessive technical details. Including univariate and multivariate techniques, Applied Time Series Analysis provides data sets and program files that support a broad range of multidisciplinary applications, distinguishing this book from others.
- Focuses on practical application of time series analysis, using step-by-step techniques and without excessive technical detail
- Supported by copious disciplinary examples, helping readers quickly adapt time series analysis to their area of study
- Covers both univariate and multivariate techniques in one volume
- Provides expert tips on, and helps mitigate common pitfalls of, powerful statistical software including EVIEWS and R
- Written in jargon-free and clear English from a master educator with 30 years+ experience explaining time series to novices
- Accompanied by a microsite with disciplinary data sets and files explaining how to build the calculations used in examples
Applied quantitative researchers, particularly econometricians and statisticians seeking to use empirical time series to study modern interdisciplinary problems in other areas. Some interest from upper division undergraduate specialist courses but mainly positioned at postgraduate (MSc / PhD) level and above
1. Time Series and Their Features
2. Transforming Time Series
3. ARMA Models for Stationary Time Series
4. ARIMA Models for Nonstationary Time Series
5. Unit Roots, Difference and Trend Stationarity, and Fractional Differencing
6. Breaking and Nonlinear Trends
7. An Introduction to Forecasting With Univariate Models
8. Unobserved Component Models, Signal Extraction, and Filters
9. Seasonality and Exponential Smoothing
10. Volatility and Generalized Autoregressive Conditional Heteroskedastic Processes
11. Nonlinear Stochastic Processes
12. Transfer Functions and Autoregressive Distributed Lag Modeling
13. Vector Autoregressions and Granger Causality
14. Error Correction, Spurious Regressions, and Cointegration
15. Vector Autoregressions With Integrated Variables, Vector Error Correction Models, and Common Trends
16. Compositional and Count Time Series
17. State Space Models
18. Some Concluding Remarks
- No. of pages:
- © Academic Press 2019
- 24th January 2019
- Academic Press
- Paperback ISBN:
- eBook ISBN:
Terence Mills is Professor of Applied Statistics and Econometrics at Loughborough University and has well over 200 publications, beginning in 1977 with a paper in the European Economic Review. He has since published in most of the international economic, economic history, econometrics, finance and statistics journals and in a range of other journals, including Journal of Climate, Climatic Change, Journal of Cosmology, International Journal of Body Composition Research, Physica A, Energy and Buildings, and Journal of Public Health. He has also written or edited almost 20 books, including a range of introductory statistics and econometric texts, handbooks on econometrics, and histories of time series analysis.
Applied Statistics and Econometrics, Loughborough University, UK
"In in his usual clear and masterful way, Terence Mills gives the reader a clear understanding of the central topics of modern time series analysis. This book is a ‘must read’ for students across a range of disciplines whose interest is in data that are generated sequentially in time. The book provides many practical computer-based examples that bring alive the key concepts in time series analysis. It will become a standard reference in its area." --Kerry Patterson, University of Reading
"Applied Time Series Analysis should prove to be very useful for practical application as it blends together the modeling and forecasting of time series data employing insightful empirical examples. This book will be useful to both practitioners as well for those with extensive experience. The exposition of material is very clear and rigorous." --Mark Wohar, University of Nebraska
Elsevier.com visitor survey
We are always looking for ways to improve customer experience on Elsevier.com.
We would like to ask you for a moment of your time to fill in a short questionnaire, at the end of your visit.
If you decide to participate, a new browser tab will open so you can complete the survey after you have completed your visit to this website.
Thanks in advance for your time.