Outlier detection algorithms for least squares time series regression

Søren Johansen, Bent Nielsen

Publikation: Working paper/Preprint Working paperForskning

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Abstract

We review recent asymptotic results on some robust methods for multiple regression. 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 asymptotic 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.
OriginalsprogEngelsk
UdgivelsesstedAarhus
UdgiverInstitut for Økonomi, Aarhus Universitet
Antal sider41
StatusUdgivet - 27 okt. 2014
NavnCREATES Research Paper
Nummer2014-39

Emneord

  • Huber-skip M-estimators, 1-step Huber-skip M-estimators, iteration, Forward Search, Impulse Indicator Saturation, Robusti…ed Least Squares, weighted and marked empirical processes, iterated martingale inequality, gauge

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