Outlier Detection Algorithms for Least Squares Time Series Regression

Soren Johansen, Bent Nielsen
2014 Social Science Research Network  
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
more » ... auge 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.
doi:10.2139/ssrn.2510281 fatcat:fhumhkycq5ccvmd4fxofwnwevi