SAS for Forecasting Time Series, Second Edition [Paperback]

John C. Brocklebank Ph.D. (Author), David A. Dickey Ph.D. (Author)

Book Description

Publication Date: April 18, 2003

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 how 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. New and updated examples in the second edition include retail sales with seasonality, ARCH models for stock prices with changing volatility, vector autoregression and cointegration models, intervention analysis for product recall data, expanded discussion of unit root tests and nonstationarity, and expanded discussion of frequency domain analysis and cycles in data.

Editorial Reviews

Review

This second edition of a 1986 publication contains additions that update this book with advances in time series forecasting. New topics include the Augmented Dickey-Fuller test, the model identification methods ESACF, SCAN and MINIC, unequal variances in time series models, and cointegration. The revisions and reorganization to chapter seven, Spectral Analysis, improve readability and comprehension. The addition of the final chapter, 'Data Mining and Forecasting', provides an introduction to the menu driven Time Series Forecasting System. SAS users who model and forecast time series data should add this book to their collection, including owners of the first edition. --Barry A. Evans, Ph.D., Manager, Forecasting GlaxoSmithKline

Drs. Brocklebank and Dickey have not only done a great job of explaining how to use SAS in forecasting time series, but have also written a good practitioner's text illustrating perils and pitfalls and how to detect them. The authors start at ground zero with illustrated explanations and build to more difficult concepts in a logical progression. For the SAS enthusiast, there is a wealth of SAS code, followed by the SAS output from that code and an abundance of graphs to illustrate what is being seen. If you need a review of time series forecasting or an understanding of how SAS treats time series forecasting, this would be a good book to have on your shelf. --Dr. Alex K. Thompson, Senior Statistician

From the Back Cover

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, STRATESPACE, 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 and vector ARMA processes using the STATE-SPACE 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.

New and updated examples in the second edition include

• Retail sales with seasonality
• ARCH models for stock prices with changing volatility
• Vector autoregression and cointegration models
• Intervention analysis for product recall data
• Expanded discussion of unit root tests and nonstationarity
• Expanded discussion of frequency domain analysis and cycles in data
• Data mining and forecasting with examples using SAS IntelliVisor
• Using the HPF procedure to automatically generate forecasts for several time series in one step

--This text refers to an alternate Paperback edition.

See all Editorial Reviews

Product Details

• Paperback: 420 pages
• Publisher: SAS Publishing; 2nd edition (April 18, 2003)
• Language: English
• ISBN-10: 1590471822
• ISBN-13: 978-1590471821
• Product Dimensions: 10.9 x 8.5 x 0.8 inches
• Shipping Weight: 2.2 pounds (View shipping rates and policies)
• Average Customer Review: 3.0 out of 5 stars  See all reviews (5 customer reviews)
• Amazon Best Sellers Rank: #442,838 in Books (See Top 100 in Books)

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Most Helpful Customer Reviews

11 of 11 people found the following review helpful:

3.0 out of 5 stars Ugh!, October 13, 2004

By 

Dennis Hanseman (Cincinnati, OH United States) - See all my reviews
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This review is from: SAS for Forecasting Time Series (Paperback)

The SAS Institute's Books by Users series contains many excellent manuals. The ones by Paul Allison (on survival analysis and on logistic regression) and by Stokes, Davis and Koch (categorical data analysis) are particularly well-written and illuminating. Unfortunately, Brocklebank and Dickey's contribution on time series analysis falls far short of the mean.
The problem is not the statistical content, which is quite reliable, but rather than explanatory style. Chapters are disorganized, with many ideas introduced before being explained. Furthermore, the authors have adopted an unfortunate habit of constantly referring to "you" (i.e., the reader). "You" will do this. "You" will decide to do that. In many case, it was far from clear why such decisions would be made.
The most serious problem, though, is the treatment of SAS code. This is supposed to be a book about ideas AND about syntax. But code is repeatedly presented with any kind of line-by-line explantion. Readers ("you" again) are left to wonder what the various elements of code mean, and how they control the computations done.
I was very disappointed with this book. Unfortunately, the only alternative is to use the SAS documentation, and that's not really a very good alternative.

7 of 7 people found the following review helpful:

4.0 out of 5 stars This manual needs a chapter on forecast accuracy., July 28, 2003

By 

Paul Sheldon Foote (Irvine, CA United States) - See all my reviews
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This review is from: SAS for Forecasting Time Series (Paperback)

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.

5 of 5 people found the following review helpful:

2.0 out of 5 stars Value added < 0, November 26, 2005

By 

Dimitri Shvorob - See all my reviews
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This review is from: SAS for Forecasting Time Series, Second Edition (Paperback)

.. the benchmark being SAS/ETS documentation. Let me recommend printing out selected chapters of SAS/ETS User's Guide - available online - describing the procedures that you (may) need, such as MODEL, FORECAST, ARIMA, VARMAX, UTC and STATESPACE.