Object Reference : Object View and Procedure Reference : Equation
  
 
fitoutliers
Detect outliers using the results of an estimated equation.
Use Tukey fences, mean/standard deviation fences, wavelet outliers, ARMA outliers or influence statistics to identify observations that may contain outliers.
Syntax
equation_name.resoutliers(options)
Options
 
sens=arg
Set the sensitivity level. Valid arguments are “low”, “medium” (default), and “high”.
nofence
Do not perform Tukey and mean/standard deviation fences.
nowave
Do not perform Wavelet Outlier detection.
noarma
Do not perform ARMA based outlier detection. ARMA outlier detection is only available for least squares equations containing ARMA terms, and is turned on by default.
noinf
Do not perform influence statistic (not including DFBETAS) based outlier detection. Influence statistic outlier detection is only available for linear least squares equations, and is turned on by default.
dfbeta
Perform DFBETA influence statistic based outlier detection. DFBETA based outlier detection is only available for linear least squares equations, and is turned off by default.
tukeyk=arg
Set the value k in the Tukey fence detection routine. This will override the value of k set by the sens= option.
meanstdevk=arg
Set the value k in the mean/standard deviation fence detection routine. This will override the value of k set by the sens= option.
wavesig=arg
Set the value false discovery rate significance value used in the Wavelet Outlier detection routine. This will override the value set by the sens= option.
armac=arg
Set the value c in the ARMA outlier detection routine. This will override the value of c set by the sens= option.
rsbound=arg
Set the value c in RSTUDENT outlier detection. This will override the value of c set by the sens= option.
hbound=arg
Set the value c in HatMatrix outlier detection. This will override the value of c set by the sens= option.
dfsbound=arg
Set the value c in DFFITS outlier detection. This will override the value of c set by the sens= option.
covbound=arg
Set the value c in CovRatio outlier detection. This will override the value of c set by the sens= option.
betabound=arg
Set the value c in DFBETA outlier detection. This will override the value of c set by the sens= option.
series=name
Create a new series in the workfile, named name, containing a value of 1 for any observations identified as an outlier, and a value of 0 for any observation identified as not an outlier.
datestring=name
Create a new string object in the workfile containing the dates (or observation identifiers) for any observations identified as an outlier.
grlabels
Turn on observation labels on the outlier graph.
prompt
Force the dialog to appear from within a program.
p
Print results.
Examples
equation eq01.ls gdpc1 c unemp
eq01.resoutliers(nofence, dfbeta, sens=low)
Estimates an equation with GDPC1 as the dependent variable, and a constant and UNEMP as regressors. Then, outlier detection on the residuals is performed, opting to not use either fence detection, but to include the dfbeta influence statistics (along with the other influence statistics included by default), and setting the sensitivity of the detection to "low".
Cross-references
See “Outlier Detection” for discussion. See also Series::outliers.