The X11 Procedure
- Overview
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Getting Started
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Syntax
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Details
Historical Development of X-11 Implementation of the X-11 Seasonal Adjustment Method Computational Details for Sliding Spans Analysis Data Requirements Missing Values Prior Daily Weights and Trading-Day Regression Adjustment for Prior Factors The YRAHEADOUT Option Effect of Backcast and Forecast Length Details of Model Selection OUT= Data Set The OUTSPAN= Data Set OUTSTB= Data Set OUTTDR= Data Set Printed Output ODS Table Names
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Examples
- References
| The YRAHEADOUT Option |
For monthly data, the YRAHEADOUT option affects only Tables C16 (regression trading-day adjustment factors), C18 (trading-day factors from combined daily weights), and D10 (seasonal factors). For quarterly data, only Table D10 is affected. Variables for all other tables have missing values for the forecast observations. The forecast values for a table are included only if that table is specified in the OUTPUT statement.
Tables C16 and C18 are calendar effects that are extrapolated by calendar composition. These factors are independent of the data once trading-day weights have been calculated. Table D10 is extrapolated by a linear combination of past values. If 
is the total number of nonmissing observations for the analysis variable, this linear combination is given by
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If the input data are monthly time series, 12 extra observations are added to the end of the output data set. (If a BY statement is used, 12 extra observations are added to the end of each BY group.) If the input data are a quarterly time series, four extra observations are added to the end of the output data set. (If a BY statement is used, four extra observations are added to each BY group.)
The DATE= variable (or _DATE_) is extrapolated for the extra observations generated by the YRAHEADOUT option, while all other ID variables will have missing values.
If ARIMA processing is requested, and if both the OUTEXTRAP and YRAHEADOUT options are specified in the PROC X11 statement, an additional 12 (or 4) observations are added to the end of output data set for monthly (or quarterly) data after the ARIMA forecasts, using the same linear combination of past values as before.