Outlier detection algorithms for least squares time series regression

Soren Johansen and Bent Nielsen

No 2014-W04, Economics Papers from Economics Group, Nuffield College, University of Oxford

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.

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. (search for similar items in EconPapers)
Pages: 38 pages
Date: 2014-09-08
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ger
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Working Paper: Outlier detection algorithms for least squares time series regression (2014)
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