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.
1. Introduction - p. ix 2. Endnotes - p. xiii 3. Chapter 1. Time Series and Their Features - p. 1 4. Chapter 2. Transforming Time Series - p. 13 5. Chapter 3. ARMA Models for Stationary Time Series - p. 31 6. Chapter 4. ARIMA Models for Nonstationary Time Series - p. 57 7. Chapter 5. Unit Roots, Difference and Trend Stationarity, and Fractional Differencing - p. 71 8. Chapter 6. Breaking and Nonlinear Trends - p. 103 9. Chapter 7. An Introduction to Forecasting With Univariate Models - p. 121 10. Chapter 8. Unobserved Component Models, Signal Extraction, and Filters - p. 131 11. Chapter 9. Seasonality and Exponential Smoothing - p. 145 12. Chapter 10. Volatility and Generalized Autoregressive Conditional Heteroskedastic Processes - p. 161 13. Chapter 11. Nonlinear Stochastic Processes - p. 173 14. Chapter 12. Transfer Functions and Autoregressive Distributed Lag Modeling - p. 201 15. Chapter 13. Vector Autoregressions and Granger Causality - p. 211 16. Chapter 14. Error Correction, Spurious Regressions, and Cointegration - p. 233 17. Chapter 15. Vector Autoregressions With Integrated Variables, Vector Error Correction Models, and Common Trends - p. 255 18. Chapter 16. Compositional and Count Time Series - p. 281 19. Chapter 17. State Space Models - p. 299 20. Chapter 18. Some Concluding Remarks - p. 311 21. References - p. 315 22. Index - p. 329
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.
1. Introduction - p. ix 2. Endnotes - p. xiii 3. Chapter 1. Time Series and Their Features - p. 1 4. Chapter 2. Transforming Time Series - p. 13 5. Chapter 3. ARMA Models for Stationary Time Series - p. 31 6. Chapter 4. ARIMA Models for Nonstationary Time Series - p. 57 7. Chapter 5. Unit Roots, Difference and Trend Stationarity, and Fractional Differencing - p. 71 8. Chapter 6. Breaking and Nonlinear Trends - p. 103 9. Chapter 7. An Introduction to Forecasting With Univariate Models - p. 121 10. Chapter 8. Unobserved Component Models, Signal Extraction, and Filters - p. 131 11. Chapter 9. Seasonality and Exponential Smoothing - p. 145 12. Chapter 10. Volatility and Generalized Autoregressive Conditional Heteroskedastic Processes - p. 161 13. Chapter 11. Nonlinear Stochastic Processes - p. 173 14. Chapter 12. Transfer Functions and Autoregressive Distributed Lag Modeling - p. 201 15. Chapter 13. Vector Autoregressions and Granger Causality - p. 211 16. Chapter 14. Error Correction, Spurious Regressions, and Cointegration - p. 233 17. Chapter 15. Vector Autoregressions With Integrated Variables, Vector Error Correction Models, and Common Trends - p. 255 18. Chapter 16. Compositional and Count Time Series - p. 281 19. Chapter 17. State Space Models - p. 299 20. Chapter 18. Some Concluding Remarks - p. 311 21. References - p. 315 22. Index - p. 329
