Applied Time Series Analysis (häftad)
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Häftad (Paperback)
Antal sidor
Academic Press
C.Mills, Terence
Black & white illustrations
233 x 167 x 18 mm
556 g
Antal komponenter
23:B&W 6 x 9 in or 229 x 152 mm Perfect Bound on White w/Gloss Lam
Applied Time Series Analysis (häftad)

Applied Time Series Analysis

A Practical Guide to Modeling and Forecasting

Häftad Engelska, 2019-01-24
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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
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  1. Applied Time Series Analysis
  2. +
  3. The Foundations of Modern Time Series Analysis

De som köpt den här boken har ofta också köpt The Foundations of Modern Time Series Analysis av Terence C Mills (inbunden).

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"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 Analysisshould 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

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Övrig information

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.


1. Introduction: stationarity, non-stationarity and trends 2. Transforming time series 3. ARMA models for stationary time series 4. ARIMA models for non-stationary time series 5. Other models of non-stationary time series: structural models, exponential a. smoothing and GARCH 6. Deterministic and stochastic trends, unit roots and fractional differencing 7. Trend decompositions and filters 8. Seasonality and seasonal time series models 9. Forecasting from univariate time series models 10. Non-linear models 11. Transfer functions and autoregressive distributed lag models 12. Cointegration and error correction 13. Vector time series: VARs, impulse response analysis and forecasting 14. Vector error correction models, common trends and common features 15. Periodic autoregressions 16. Compositional time series