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

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