SEATSDECOMP OUT=
SASdataset <options> ;
The SEATSDECOMP statement creates an output data set (named by the OUT= option) that contains the SEATS decomposition series.
The following is an example of a VAR statement and a SEATSDECOMP statement:
var sales costs;
seatsdecomp out=SEATS_DECOMP;
The default variable name used in the output data set is the input variable name followed by an underscore and the corresponding
table name. Because the B1 series is used as the original input series for the SEATS decomposition, the output data set SEATS_DECOMP
from the example will contain the seasonal deomposition variables in the following order:
 sales_OS

contains the Table B1 values for the variable sales
.
 sales_SC

contains the SEATS decomposition seasonal component for the variable sales
.
 sales_TC

contains the SEATS trend component values for the variable sales
.
 sales_SA

contains the SEATS seasonally adjusted series for the variable sales
.
 sales_IC

contains the SEATS irregular component for the variable sales
.
 costs_OS

contains the Table B1 values for the variable costs
.
 costs_SC

contains the SEATS decomposition seasonal component for the variable costs
.
 costs_TC

contains the SEATS trend component values for the variable costs
.
 costs_SA

contains the SEATS seasonally adjusted series for the variable costs
.
 costs_IC

contains the SEATS irregular component for the variable costs
.
If necessary, the variable name is shortened so that the component name can be added. If you specify the DATE= variable in
the PROC X12 statement, then that variable is included in the output data set; otherwise, a variable named _DATE_
is written to the OUT= data set as the date identifier. For further information about the output data set, see SEATSDECOMP OUT= Data Set.
You can specify the following options in the SEATSDECOMP statement:

LEAD=value

specifies the number of periods ahead to forecast for a regARIMA extension of the series. The default is twice the number
of periods in a year (8 or 24), and the maximum is 120. In the SEATS computations, the number of backcasts and forecasts are
the same, and the minimum number is also dependent on the ARIMA model orders. For more information, see the section SEATS Decomposition. If you specify a LEAD= value that is less than the default, then the number of forecasts specified in the LEAD= option are
displayed in the OUT= data set. If the value of the LEAD= option and NBACKCAST= options in the FORECAST statement are less
than the required number for SEATS decomposition, then the values of the LEAD= and NBACKCAST= options in the FORECAST statement
are increased.

NBACKCAST=value
BACKCAST=value
NBACK=value

specifies the number of periods to backcast for a regARIMA extension of the series. The default is twice the number of periods in a year (8 or 24), and the maximum is 120. In the SEATS computations, the number
of backcasts and forecasts are the same, and the minimum number is also dependent on the ARIMA model orders. For more information,
see the section SEATS Decomposition. If you specify a NBACKCAST= value that is less than the default, then the number of backcasts specified in the NBACKCAST=
option are displayed in the OUT= data set. If the value of the LEAD= option and NBACKCAST= option specified in the FORECAST
statement are less than the required number for SEATS decomposition when SEATSDECOMP is specified, then the value of LEAD=
and NBACKCAST= in the FORECAST statement will be increased.

OUT=SASdataset

names the data set to contain the SEATS decomposition series: original series, seasonal component, trend component, seasonally
adjusted series, irregular component. If the OUT= option is omitted, the data set is named using the default DATAn convention.

YEARSEAS
YRSEAS

specifies that two additional variables be added to the OUT= data set: _YEAR_
and _SEASON_
. The variable _YEAR_
contains the year of the date that identifies the observation. The variable _SEASON_
contains the month for monthly data, or quarter for quarterly data, of the date that identifies the observation. For monthly
data, the value of _SEASON_
is between 1 and 12. For quarterly data, the value of _SEASON_
is between 1 and 4. The _YEAR_
and _SEASON_
variables are useful when you create seasonal plots.
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