OUTLIER options ;

The OUTLIER statement specifies that the X12 procedure perform automatic detection of additive (point) outliers, temporary change outliers, level shifts, or any combination of the three when using the specified model. After outliers are identified, the appropriate regression variables are incorporated into the model as "Automatically Identified Outliers," and the model is reestimated. This procedure is repeated until no additional outliers are found.

The OUTLIER statement also identifies potential outliers and lists them in the table "Potential Outliers" in the displayed output. Potential outliers are identified by decreasing the critical value by 0.5.

In the output, the default initial critical values used for outlier detection in a given analysis are displayed in the table "Critical Values to Use in Outlier Detection." Outliers that are detected and incorporated into the model are displayed in the output in the table "Regression Model Parameter Estimates," where the regression variable is listed as "Automatically Identified."

The following options can appear in the OUTLIER statement.

SPAN=(mmmyy ,mmmyy )

SPAN=(’yyQq’ ,’yyQq’ )

gives the dates of the first and last observations to define a subset for searching for outliers. A single date in parentheses is interpreted to be the starting date of the subset. To specify only the ending date, use SPAN=(,mmmyy ) or SPAN=(,’yyQq’ ). If the starting or ending date is omitted, then the first or last date, respectively, of the input data set is assumed. A four-digit year can be specified; if a two-digit year is specified, the value specified in the YEARCUTOFF= SAS system option applies.

TYPE=NONE

TYPE=(outlier types)

lists the outlier types to be detected by the automatic outlier identification method. TYPE=NONE turns off outlier detection. The valid outlier types are AO, LS, and TC. The default is TYPE=(AO LS).

CV=value

specifies an initial critical value to use for detection of all types of outliers. The absolute value of the t statistic associated with an outlier parameter estimate is compared with the critical value to determine the significance of the outlier. If the CV= option is not specified, then the default initial critical value is computed using a formula presented by Ljung (1993), which is based on the number of observations or model span used in the analysis. Table 32.2 gives default critical values for various series lengths. Increasing the critical value decreases the sensitivity of the outlier detection routine and can reduce the number of observations treated as outliers. The automatic model identification process might lower the critical value by a certain percentage, if the automatic model identification process fails to identify an acceptable model.

Table 32.2 Default Critical Values for Outlier Identification

Number of Observations	Outlier Critical Value
1	1.96
2	2.24
3	2.44
4	2.62
5	2.74
6	2.84
7	2.92
8	2.99
9	3.04
10	3.09
11	3.13
12	3.16
24	3.42
36	3.55
48	3.63
72	3.73
96	3.80
120	3.85
144	3.89
168	3.92
192	3.95
216	3.97
240	3.99
264	4.01
288	4.03
312	4.04
336	4.05
360	4.07

AOCV=value

specifies a critical value to use for additive (point) outliers. If AOCV is specified, this value overrides any default critical value for AO outliers. See the CV= option for more details.

LSCV=value

specifies a critical value to use for level shift outliers. If LSCV is specified, this value overrides any default critical value for LS outliers. See the CV= option for more details.

TCCV=value

specifies a critical value to use for temporary change outliers. If TCCV is specified, this value overrides any default critical value for TC outliers. See the CV= option for more details.