Applied Time Series Analysis

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.

Autorentext
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.

Inhalt

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