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

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 detailSupported by copious disciplinary examples, helping readers quickly adapt time series analysis to their area of studyCovers both univariate and multivariate techniques in one volumeProvides expert tips on, and helps mitigate common pitfalls of, powerful statistical software including EVIEWS and RWritten in jargon-free and clear English from a master educator with 30 years+ experience explaining time series to novicesAccompanied by a microsite with disciplinary data sets and files explaining how to build the calculations used in examples

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