The X12 Procedure
- Overview
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Getting Started
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SyntaxFunctional SummaryPROC X12 StatementADJUST StatementARIMA StatementAUTOMDL StatementBY StatementCHECK StatementESTIMATE StatementEVENT StatementFORECAST StatementID StatementIDENTIFY StatementINPUT StatementOUTLIER StatementOUTPUT StatementPICKMDL StatementREGRESSION StatementSEATSDECOMP StatementTABLES StatementTRANSFORM StatementUSERDEFINED StatementVAR StatementX11 Statement
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DetailsData RequirementsMissing ValuesSAS Predefined EventsUser-Defined Regression VariablesCombined Test for the Presence of Identifiable SeasonalityComputationsPICKMDL Model SelectionSEATS DecompositionDisplayed Output, ODS Table Names, and OUTPUT Tablename KeywordsUsing Auxiliary Variables to Subset Output Data SetsODS GraphicsOUT= Data SetSEATSDECOMP OUT= Data SetSpecial Data Sets
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ExamplesARIMA Model IdentificationModel EstimationSeasonal AdjustmentRegARIMA Automatic Model SelectionAutomatic Outlier DetectionUser-Defined RegressorsMDLINFOIN= and MDLINFOOUT= Data SetsSetting Regression ParametersCreating an MDLINFO= Data Set for Use with the PICKMDL StatementIllustration of ODS GraphicsAUXDATA= Data Set
- References
PICKMDL Statement
PICKMDL options ;
The PICKMDL statement uses models specified in the MDLINFOIN= data set to choose a regARIMA model. The MDLINFOIN= data set can include a variable that identifies different models. All
observations with the same value for the model identification variable are considered to be relevant to the same model. A
single model can be considered to consist of all the observations for a BY group that consists of the BY variables (if any),
the _NAME_ variable if it exists, and the MDLVAR= variable. The default variable name that identifies the model is _MODEL_. Example 38.9 demonstrates the use of the PICKMDL statement.
For more information about using the U.S. Census Bureau’s PICKMDL method for model selection, see the section PICKMDL Model Selection .
You can specify the following options in the PICKMDL statement: