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State Space Modeling Essentials Using the SSM Procedure in SAS/ETS®
This course covers the fundamentals of building and applying state space models using the SSM procedure (SAS/ETS). Students are presented with an overview of the model and learn advantages of the State Space approach. The course also describes fundamental model details, presents some straightforward examples of specifying and fitting models using the SSM procedure, and considers estimation in SSM, focusing on the Kalman filter and related details. The course concludes with a variety of SSM modeling applications, focused mainly on time series. Learn How To identify the various parts of the SSM model and specify them in the SSM procedure syntax fit basic models and use them for visualization of components of variation in the data fit advanced models including dynamic regression (transfer function) and multivariate time series models. Who Should Attend Time series modelers and analysts who want to take advantage of a flexible and visual approach to modeling sequential data Prerequisites Students should be comfortable with linear modeling ideas and have some experience with time series models such as Unobserved Components models or ARIMAX. SAS Products Covered SAS/ETS Course Outline State Space Models introduction reasons for using a state space model state space model frameworkBasic Modeling Using the SSM Procedureidentifying state space model components fitting basic modelsIntroduction to the Kalman Filter and Estimation in the SSM Frameworkstate space models and regression filtering diffuse starting values filtering results smoothed estimatesMore Modeling Examples Using the SSM Procedureaccommodating an endogenous input variable in an SSM Demonstration: Specifying and estimating a transfer function model in the SSM procedure a multivariate model cointegration
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