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.

• 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