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 Model Selection
You can request that the X-12-ARIMA method select a model in a manner similar to the method used in X-11-ARIMA (Dagum, 1988, 1983) by specifying the PICKMDL statement. Information about this model selection (PICKMDL) is based on the description in the X-12-ARIMA Reference Manual (U.S. Bureau of the Census, 2009c).
The default settings for the PICKMDL automatic model selection method classify a model as acceptable if all of the following conditions are true:
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The absolute average percentage error of the extrapolated values within the last three years of data is less than 15%.
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The p-value is greater than 5% for the fitted model’s Ljung-Box Q statistic test of the lack of correlation in the model’s residuals.
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There are no signs of overdifferencing. Overdifferencing is indicated if the sum of the nonseasonal MA parameter estimates (for models with at least one nonseasonal difference) is greater than 0.9.
No model is selected when none of the models in the MDLINFOIN= data set is acceptable.
The regARIMA model consists of a transformation, a regression component, and an ARIMA model component. For each series, the following conditions hold:
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If no regression is specified in the MDLINFOIN= data set model but regressors are specified using the INPUT, EVENT, or REGRESSION statements, then the ARIMA models from the MDLINFOIN= data set are tested in conjunction with the regression variables specified by the INPUT, EVENT, and REGRESSION statements.
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If no ARIMA model is specified in the MDLINFOIN= data set but an ARIMA model is specified using an ARIMA statement or TRANSFORM statement, then the regression information from each model specified in the MDLINFOIN= data set is used in conjunction with the ARIMA model specified by the TRANSFORM and ARIMA statements.
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If no model information is specifed in the MDLINFOIN= data set, then any model information specified by the TRANSFORM, INPUT, REGRESSION, EVENT, and ARIMA statements is used, and the PICKMDL statement is not in effect for that series.