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
PROC X12 can process a series with missing values.
Missing values in a series are considered to be one of two types:
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A leading or trailing missing value occurs before the first nonmissing value or after the last nonmissing value, respectively, in the span of a series. The span of a series can be determined either explicitly by the SPAN= option or implicitly by the START= or DATE= option in the PROC X12 statement. By default, leading and trailing missing values are ignored. If you specify the NOTRIMMISS option in the PROC X12 statement, PROC X12 processes leading and trailing missing values according to the X-12-ARIMA missing value method.
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An embedded missing value occurs between the first nonmissing value and the last nonmissing value in the span of the series. PROC X12 processes embedded missing values according to the X-12-ARIMA missing value method.
When the X-12-ARIMA method encounters a missing value, it inserts an additive outlier for the missing observation into the set of regression variables for the model of the series and then replaces the missing observation with a value large enough to be considered an outlier during model estimation. After the regARIMA model is estimated, the X-12-ARIMA method adjusts the original series by using factors that are generated from these missing value outlier regressors. The adjusted values are estimates of the missing values, and the adjusted series is displayed in Table MV1. The X-12-ARIMA missing value method requires the use of a regARIMA model to replace the missing values. Thus, either an ARIMA or AUTOMDL statement or the MDLINFOIN= option in the PROC X12 statement must be specified if there are embedded missing values in the time series.