Template-type: ReDIF-Paper 1.0
Author-Name: Søren Johansen
Author-Workplace-Name: Dept of Economics, University of Copenhagen and CREATES, Dept of Economics and
Business, Aarhus University
Author-Email: soren.johansen@econ.ku.dk
Author-Name: Bent Nielsen
Author-Workplace-Name: Nuffield College and Dept of Economics
Author-Email: bent.nielsen@nuffield.ox.ac.uk.
Title: Outlier detection algorithms for least squares time series regression 
Abstract: We review recent asymptotic results on some robust methods for multiple regres-
sion. The regressors include stationary and non-stationary time series as well as polynomial
terms. The methods include the Huber-skip M-estimator, 1-step Huber-skip M-estimators,
in particular the Impulse Indicator Saturation, iterated 1-step Huber-skip M-estimators and
the Forward Search. These methods classify observations as outliers or not. From the as-
ymptotic results we establish a new asymptotic theory for the gauge of these methods, which
is the expected frequency of falsely detected outliers. The asymptotic theory involves normal
distribution results and Poisson distribution results. The theory is applied to a time series
data set.
X-Classification-JEL: 
Keywords:Huber-skip M-estimators, 1-step Huber-skip M-estimators, iteration, Forward
Search, Impulse Indicator Saturation, Robusti?ed Least Squares, weighted and marked em-
pirical processes, iterated martingale inequality, gauge.
Length: 38 pages
Creation-Date: 2014-09-08
Number: 2014-W04
File-URL: http://www.nuffield.ox.ac.uk/economics/papers/2014/OutlierDetectionAlgorithms.pdf
File-Format: application/pdf
Handle: RePEc:nuf:econwp:1404