Applied Time Series Analysis: A Practical Guide to Modeling and Forecasting

by Terence C. Mills

Overview

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.

Product Details

ISBN-13:	9780128131176
Publisher:	Elsevier Science
Publication date:	01/24/2019
Pages:	354
Product dimensions:	6.00(w) x 9.00(h) x (d)

About the Author

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.

Table of Contents

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

What People are Saying About This

From the Publisher

A "how-to" manual for researchers across discliplines who need an introduction to the methods, software, and concepts of econometrics and statistics

Editorial Reviews

"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

From the Publisher