SAS for Forecasting Time Series

by David A Dickey, John C. Brocklebank, David A. Dickey, et al.

Book Overview

To use statistical methods and SAS applications to forecast the future values of data taken over time, you need only follow this thoroughly updated classic on the subject. With this third edition of... This description may be from another edition of this product.

• Format: Paperback
• Language: English
• ISBN: 1590471822
• ISBN13: 9781590471821
• Release Date: April 2003
• Publisher: SAS Institute
• Length: 398 Pages
• Weight: 2.25 lbs.
• Dimensions: 0.8" x 8.5" x 10.9"

Customer Reviews

Excellent

If you're interested in advanced methods of forecasting time series data using SAS then this is the book to have. It is loaded with examples and interpretation of output as well as a nice concise explanation of theory. Everything you would expect from such renowned authors.

This manual needs a chapter on forecast accuracy.

While the publishers describe SAS for Forecasting Time Series as a manual, the authors have provided more than SAS statements and the resulting outputs. Theoretical explanations, equations, and matrix algebra forms of equations fill the book. This superb manual is the product of the Research and Development Director of Analytic Solutions at SAS and of the Professor of Statistics who was the co-inventor of the Dickey-Fuller test. In addition to the coverage of the essential univariate and multivariate time series analysis topics (e.g., ARIMA models), the authors included entire chapters or large portions of chapters on: Cointegration, State Space Modeling, Spectral Analysis, and Data Mining.My only disappointment with this manual was the lack of an entire chapter on forecast accuracy. Four pages of references did not include a single reference to articles about forecasting competitions. The authors could have: (1) held back recent data in their examples (2) made forecasts with their best models (3) explained how to identify significant changes over time in error terms, standard errors, and in correlations (4) explained when and how to re-calculate model parameters (5) discussed the choice of unbiased forecast accuracy measures for comparing forecasts from ARIMA and regression models.