SAS for Forecasting Time Series, Second Edition

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

Book Overview

In this second edition of the indispensable SAS for Forecasting Time Series, Brocklebank and Dickey show you how SAS performs univariate and multivariate time series analysis. Taking a tutorial approach, the authors focus on the procedures that most effectively bring results: the advanced procedures ARIMA, SPECTRA, STATESPACE, and VARMAX. They demonstrate the interrelationship of SAS/ETS procedures with a discussion of how the choice of a procedure depends on the data to be analyzed and the results desired. With this book, you will learn to model and forecast simple autoregressive (AR) processes using PROC ARIMA, and you will learn to fit autoregressive and vector ARMA processes using the STATESPACE and VARMAX procedures. Other topics covered include detecting sinusoidal components in time series models, performing bivariate cross-spectral analysis, and comparing these frequency-based results with the time domain transfer function methodology. Intermediate to advanced data analysts who use SAS software to perform univariate and multivariate time series analyses. This book is an ideal supplemental text for students in undergraduate- and graduate-level statistics courses. Book jacket.

• Format: Paperback
• Language: English
• ISBN: 1590471822
• ISBN13: 9781590471821
• Release Date: April 2003
• Publisher: SAS Institute
• Length: 420 Pages
• Weight: 2.25 lbs.
• Dimensions: 10.8" x 1.0" x 8.3"

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.