resoutliers |

Detect outliers in the residuals or regressors of the equation.

Use Tukey fences, mean/standard deviation fences, wavelet outliers, ARMA outliers or influence statistic detection methods 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. |

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".