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
Missing Values
For a variety of reasons the data might contain missing response and predictor values. Before starting the analysis of a particular BY group, SSM procedure makes an internal copy of the data. The actual analysis is done by using this copy. The data in the copy are first examined for missing values in the response, predictor, and the ID variables. No missing values are permitted in the ID variable (if it is specified). If all the missing values are associated with only the response variables, then the internal copy of the data is not altered. However, if any of the predictors are found to contain missing values, the internal copy of the data is altered as follows: any missing predictor value is replaced by 0, and the response values that are dependent on that predictor in the corresponding row are set to missing. These missing response values are called the induced missing values. The reported analysis is based on the (possibly altered) internal copy of the BY group.