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.
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SAS for Forecasting Time Series, Second Edition In this second edition of the indispensable series, Brocklebank and Dickey describe 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.
"The new material and the update of the excellent 1E, now 17 years in the past, certainly make the 2E a necessary purchase for any user of SAS time series modeling methods." ( Technometrics, Vol. 46, No. 1, February 2004)
Taking a tutorial approach, the authors focus on procedures that most effectively bring results (Zentralblatt MATH, April 2007)
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