The X13 Procedure
- Overview
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Getting Started
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SyntaxFunctional SummaryPROC X13 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 KeywordsTable D 8.BUsing 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
Overview: X13 Procedure
The X13 procedure is an adaptation of the US Bureau of the Census X-13ARIMA-SEATS seasonal adjustment program (US Bureau of the Census 2013c). The X-13ARIMA-SEATS program was developed by the Time Series Staff of the Statistical Research Division, US Census Bureau, by incorporating the SEATS method into the X-12-ARIMA seasonal adjustment program. The X-12-ARIMA seasonal adjustment program contains components developed from Statistics Canada’s X-11-ARIMA program (US Bureau of the Census 2010). The X-12-ARIMA automatic modeling method and the SEATS method are based on the work of Gómez and Maravall (1997a, 1997b).
The new X-13ARIMA-SEATS program incorporates the X-12-ARIMA functionality. It also incorporates improvements on X-12-ARIMA methods. Because the X-12-ARIMA methods and improvements are available in X-13ARIMA-SEATS, the new X13 procedure and the existing X12 procedure use the same X-13ARIMA-SEATS methodology, and PROC X12 and PROC X13 are aliases for the same procedure.
The version of PROC X13 documented here was produced by converting the US Census Bureau’s FORTRAN code to the SAS development language and adding typical SAS procedure syntax. This conversion work was performed by SAS and resulted in the X13 procedure. Although several features were added during the conversion, credit for the statistical aspects and general methodology of the X13 procedure belongs to the US Census Bureau.
The X13 procedure seasonally adjusts monthly or quarterly time series. The procedure makes additive or multiplicative adjustments and creates an output data set that contains the adjusted time series and intermediate calculations.
The X-13ARIMA-SEATS program includes the X-12-ARIMA program, which combines the capabilities of the X-11 program (Shiskin, Young, and Musgrave 1967) and the X-11-ARIMA/88 program (Dagum 1988) and also introduces some new features (Findley et al. 1998). One of the main enhancements in the X-12-ARIMA program involves the use of a regARIMA model, a regression model with ARIMA (autoregressive integrated moving average) errors. Thus, the X-12-ARIMA program contains methods developed by both the US Census Bureau and Statistics Canada. In addition, the X-12-ARIMA automatic modeling routine is based on the TRAMO (time series regression with ARIMA noise, missing values, and outliers) method (Gómez and Maravall 1997a, 1997b). The four major components of the X-12-ARIMA program are regARIMA modeling, model diagnostics, seasonal adjustment that uses enhanced X-11 methodology, and post-adjustment diagnostics. Statistics Canada’s X-11 method fits an ARIMA model to the original series, and then uses the model forecasts to extend the original series. This extended series is then seasonally adjusted by the standard X-11 seasonal adjustment method. The extension of the series improves the estimation of the seasonal factors and reduces revisions to the seasonally adjusted series as new data become available.
Seasonal adjustment of a series is based on the assumption that seasonal fluctuations can be measured in the original series,
, 
…, n, and separated from trend cycle, trading day, and irregular fluctuations. The seasonal component of this time series, 
, is defined as the intrayear variation that is repeated consistently or evolves slowly from year to year (Hillmer and Tiao
1982). The trend cycle component, 
, includes variation that is attributed to the long-term trend, the business cycle, and other long-term cyclical factors.
The trading day component, 
, is the variation that can be attributed to the composition of the calendar. The irregular component, 
, is the residual variation. Many economic time series are related in a multiplicative fashion (
). Other economic series are related in an additive fashion (
). A seasonally adjusted time series, 
or 
, consists of only the trend cycle and irregular components. For more information about the X-11 seasonal adjustment method,
see Ladiray and Quenneville (2001).
Graphics are now available with the X13 procedure. For more information, see the section ODS Graphics.