The SSM Procedure (Experimental)
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Overview
- Getting Started
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Syntax
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DetailsState Space Model and NotationTypes of Data OrganizationOverview of Model Specification SyntaxLikelihood, Filtering, and SmoothingContrasting PROC SSM with Other SAS Procedures Predefined Trend ModelsPredefined Structural ModelsCovariance ParameterizationMissing ValuesComputational IssuesDisplayed OutputODS Table NamesODS Graph NamesOUT= Data Set
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ExamplesBivariate Basic Structural Model Two-Way Random-Effects and Autoregressive Model for Panel DataBackcasting, Forecasting, and InterpolationSmoothing of Repeated Measures DataA User-Defined Trend ModelModel with Multiple ARIMA ComponentsDynamic Factor ModelingDiagnostic PlotsVariable Bandwidth Smoothing
- References
Details
- State Space Model and Notation
- Types of Data Organization
- Overview of Model Specification Syntax
- Likelihood, Filtering, and Smoothing
- Contrasting PROC SSM with Other SAS Procedures
- Predefined Trend Models
- Predefined Structural Models
- Covariance Parameterization
- Missing Values
- Computational Issues
- Displayed Output
- ODS Table Names
- ODS Graph Names
- OUT= Data Set
Throughout this section, vectors and matrices are denoted by bold-faced letters. Generally, Greek letters (such as 
, 
, and 
) denote unobserved or latent quantities—often estimated from the data—that represent model parameters, latent states, or
noise variables. Capital letters such as 
, 
, and 
are used to denote the observed data variables. Whenever it is unambiguous, it is assumed that the matrices have appropriate
dimensions when they are being multiplied—in particular, the vectors behave as column vectors or row vectors as the need arises.
On many occasions, matrices are described inline—that is, they are described as parenthesized lists, in a rowwise fashion,
with the rows separated by a comma. The term “dot product” is used to describe the scalar that results from the product of a row vector with a (conforming) column vector.