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 ModelsModels with Dependent LagsCovariance 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
Background
State space models are widely used in a variety of fields such as engineering, statistics, econometrics, and agriculture. There are numerous references that deal with state space modeling, particularly with the state space modeling of time series data. State space modeling of longitudinal data has received a little less attention. The primary reference for the modeling techniques implemented in the SSM procedure is Harvey (1989). It contains treatment of both the time series and longitudinal data. Other useful books about this subject are: Durbin and Koopman (2001); Jones (1993); Anderson and Moore (1979). In addition, informative articles about state space modeling of longitudinal data include Wecker and Ansley (1983); Kohn and Ansley (1991); De Jong and Mazzi (2001); Eubank, Huang, and Wang (2003); Selukar (2015). For the implementation details of the diffuse Kalman filter and smoother (the main computational tool used by the SSM procedure), the main references are a series of articles (De Jong 1989, 1991; De Jong and Chu-Chun-Lin 2003) and the references therein.