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...

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