Outlier detection algorithms for least squares time series regression

Soren Johansen and Bent Nielsen

CREATES Research Papers from Department of Economics and Business Economics, Aarhus University

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.

Keywords: 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 (search for similar items in EconPapers)
JEL-codes: C22 C52 (search for similar items in EconPapers)
Pages: 39
Date: 2014-09-08
New Economics Papers: this item is included in nep-ets
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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