Applied Time Series Analysis
A Practical Guide to Modeling and Forecasting
- 1 Edición - 22 de enero de 2019
- Última edición
- Autor: Terence C. Mills
- Idioma: Inglés
Written for those who need an introduction, Applied Time Series Analysis reviews applications of the popular econometric analysis technique across disciplines. Carefully balancing… Leer más
Descripción
Descripción
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.
Puntos claves
Puntos claves
- 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
De interès para
De interès para
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
Índice
Índice
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
Reseñas
Reseñas
"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
Detalles del producto
Detalles del producto
- Edición: 1
- Última edición
- Publicado: 24 de enero de 2019
- Idioma: Inglés
Sobre el autor
Sobre el autor
TM