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
After the model-fitting phase, the filtering process is repeated again to produce the model-based one-step-ahead response
variable forecasts (
), residuals (
), and their standard errors (
). In addition, one-step-ahead forecasts of the components that are specified in the MODEL statements, and any other user-defined
linear combinations of 
, are also produced. These forecasts are set to missing as long as the index 
(that is, until the filtering process is initialized). If the filtering process remains uninitialized, then all the quantities
that are related to the one-step-ahead forecast (such as and ) are reported as missing. When the fitted model is appropriate, the one-step-ahead residuals form a sequence of uncorrelated normal variates. This fact can be used during model diagnostic process.