Elements of Forecasting (Book Only)

Diebold, Francis X.

ISBN 10: 0324359047 / ISBN 13: 9780324359046

Published by Cengage Learning, 2006

About this Item

Seller Inventory # 0324359047-3-34370798

Bibliographic Details

• Title: Elements of Forecasting (Book Only)
• Publisher: Cengage Learning
• Publication Date: 2006
• Language: English
• ISBN 10: 0324359047
• ISBN 13: 9780324359046
• Binding: hardcover
• Edition: 4th Edition
• Condition: Good
• Weight: 28 ounces

About this title

Synopsis

Elements of Forecasting is a concise, modern survey of business and economics forecasting methods. Written by a leading expert on forecasting, it focuses on the core techniques of widest applicability and umes only an elementary background in statistics. It is applications-oriented and illustrates all methods with detailed examples and case studies. -- Covers standard material (trend, seasonality, cycles) as well as more modern topics such as model selection, volatility models, unit roots and stochastic trends, vector autoregressions, and cointegration -- Highly applications-oriented, and numerous detailed real-world examples chosen from a variety of fields (including economics, economics, public policy, and engineering) to illustrate all methods. -- Integrates modern modeling and forecasting software, using Eviews output throughout to illustrate concepts. All the data yzed is included on a disk packaged with the book -- Drives home the limits of forecasting through realistic examples in which not everything works perfectly

Review

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.