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 INDICE: 1. Introduction: stationarity, non-stationarity and trends 2. Transforming time series 3. ARMA models for stationary time series 4. ARIMA models for non-stationary time series 5. Other models of non-stationary time series: structural models, exponential a. smoothing and GARCH 6. Deterministic and stochastic trends, unit roots and fractional differencing 7. Trend decompositions and filters 8. Seasonality and seasonal time series models 9. Forecasting from univariate time series models 10. Non-linear models 11. Transfer functions and autoregressive distributed lag models 12. Cointegration and error correction 13. Vector time series: VARs, impulse response analysis and forecasting 14. Vector error correction models, common trends and common features 15. Periodic autoregressions 16. Compositional time series

• ISBN: 978-0-12-813117-6
• Editorial: Academic Press
• Encuadernacion: Rústica
• Páginas: 432
• Fecha Publicación: 25/01/2019
• Nº Volúmenes: 1
• Idioma: Inglés