Elements of Forecasting 4th Edition by Francis X. Diebold

Elements of Forecasting 4th Edition by Francis X. Diebold focuses on the core techniques of widest applicability. The writer illustrates all methods with detailed real-world functions, many of them international in taste, designed to imitate typical forecasting situations.

The text is excellent from instructor’s perspective. It's targeted and comprehensive. The text is empirically oriented. It covers major issues of time-series econometrics on the undergraduate level. Including several comprehensive applications is a singular and outstanding characteristic of this book. Coverage and organization of the book are excellent and focused on the coed while giving many pointers and references to advanced material and even present research.

The use of practical examples (using the Eviews software) and the availability of a data disk makes this a very relevant guide for practitioners. There is a good section on graphical analysis and modelling of cycles using AR and MA processes. The mathematics is kept simple and clear, intuitive explanations are given throughout. The treatment of unit roots, cointegration and other advanced materials is quite sketchy but I guess that is to be expected in an introductory text. With the level of clarity evident throughout this book, I certainly hope Diebold follows up with another book on more advanced forecasting techniques.

Table of Contents

1. Introduction to Forecasting: Applications, Methods, Books, Journals, and Software. Appendix: The Linear Regression Model.
2. Six Considerations Basic to Successful Forecasting.
3. Statistical Graphics for Forecasting.
4. Modeling and Forecasting Trend.
5. Modeling and Forecasting Seasonality.
6. Characterizing Cycles.
7. Modeling Cycles: MA, AR, and ARMA Models.
8. Forecasting Cycles.
9. Putting it All Together: A Forecasting Model with Trend, Seasonal, and Cyclical Components.
10. Forecasting with Regression Models.
11. Evaluating and Combining Forecasts.
12. Unit Roots, Stochastic Trends, ARIMA Forecasting Models, and Smoothing.
13. Volatility Measurement, Modeling and Forecasting.

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