® 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
Chapter 1– Overview of Time Series.
Chapter 2– Simple Models: Autoregression.
Chapter 3– The General ARIMA Model.
Chapter 4– The ARIMA Model: Introductory Applications.
Chapter 5– The ARIMA Model: Special Applications.
Chapter 6– State Space Modeling.
Chapter 7– Spectral Analysis.
Chapter 8– Data Mining and Forecasting.
Taking a tutorial approach, the authors focus on procedures that most effectively bring results (Zentralblatt MATH, April 2007)