The SSM Procedure
-
Overview
- Getting Started
-
Syntax
-
DetailsState Space Model and NotationTypes of Sequence DataOverview of Model Specification SyntaxFiltering, Smoothing, Likelihood, and Structural Break DetectionEstimation of User-Specified Linear Combination of State ElementsContrasting PROC SSM with Other SAS Procedures Predefined Trend ModelsPredefined Structural ModelsModels with Dependent LagsCovariance ParameterizationMissing ValuesComputational IssuesDisplayed OutputODS Table NamesODS Graph NamesOUT= Data Set
-
ExamplesBivariate Basic Structural Model Panel Data: Random-Effects and Autoregressive ModelsBackcasting, Forecasting, and InterpolationLongitudinal Data: Smoothing of Repeated MeasuresA User-Defined Trend ModelModel with Multiple ARIMA ComponentsDynamic Factor ModelingDiagnostic Plots and Structural Break AnalysisLongitudinal Data: Variable Bandwidth SmoothingA Transfer Function Model for the Gas Furnace DataPanel Data: Dynamic Panel Model for the Cigar DataMultivariate Modeling: Long-Term Temperature TrendsBivariate Model: Sales of Mink and Muskrat FursFactor Model: Now-Casting the US EconomyLongitudinal Data: Lung Function Analysis
- References
Overview of Model Specification Syntax
An SSM specification involves the description of the terms in the observation equation, the state transition equation, and
the initial condition. For example, the response variables, the predictor variables, and the elements of the state transition
matrix 
must somehow be specified. The SSM procedure syntax is designed so that little effort is needed to specify the more commonly
needed models, while a highly flexible language is available for specifying more complex models. Two syntax features help
achieve this goal: the ability to build a complex specification by combining simpler subspecifications, and a programming
language for creating lists of variables to be used later in the model specification.
The SSM procedure statements can be divided into two classes:
-
programming statements , which are used to create lists of variables that can be used for a variety purposes (for example, as the elements of the model system matrices)
-
statements specific to the SSM procedure that formulate the state space model and control its other aspects such as the input data specification and the resulting output
Since the matrices involved in the model specification can be specified as lists of variables, which you separately create by using the programming statements, you can finely control all aspects of the model specification. These programming statements permit the use of most DATA step language features such as the conditional logic (IF-THEN-ELSE), array type variables, and all the mathematical functions available in the DATA step. You can also use programming statements to define predictor variables on the fly.