The X13 procedure is an adaptation of the US Bureau of the Census X-13ARIMA-SEATS seasonal adjustment program. 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.

The 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 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 and the X-11-ARIMA/88 program and also introduces some new features. 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. 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.

For further details, see the *SAS/ETS ^{®} User's Guide*

- Example 45.1: ARIMA Model Identification
- Example 45.2: Model Estimation
- Example 45.3: Seasonal Adjustment
- Example 45.4: RegARIMA Automatic Model Selection
- Example 45.5: Automatic Outlier Detection
- Example 45.6: User-Defined Regressors
- Example 45.7: MDLINFOIN= and MDLINFOOUT= Data Sets
- Example 45.8: Setting Regression Parameters
- Example 45.9: Creating an MDLINFO= Data Set for Use with the PICKMDL Statement
- Example 45.10: Illustration of ODS Graphics
- Example 45.11: AUXDATA= Data Set