The SSM Procedure
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Overview
- Getting Started
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Syntax
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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 ModelsCovariance ParameterizationMissing ValuesComputational IssuesDisplayed OutputODS Table NamesODS Graph NamesOUT= Data Set
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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
The statistical models that govern the predefined trend components available in the SSM procedure are divided into two groups:
models that are applicable to equally spaced data (possibly with replication), and models that are applicable more generally
(the irregular data type). Each trend component can be described as a dot product 
for some (time-invariant) vector 
and a state vector 
. The component specification is complete after the vector is specified and the system matrices that govern the equations of are specified. For trend models for regular data, all the system matrices are time-invariant. For irregular data, 
and 
depend on the spacing between the distinct time points: 
.