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THE IMPACT OF EXTREME OBSERVATIONS ON SIMPLE FORECASTING METHODS *
Author(s) -
Kucukemiroglu Orsay,
Ord Keith
Publication year - 1985
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/j.1540-5915.1985.tb01681.x
Subject(s) - outlier , autoregressive model , estimator , least absolute deviations , simple (philosophy) , econometrics , computer science , least squares function approximation , statistics , mathematics , philosophy , epistemology
In general linear modeling, an alternative to the method of least squares (LS) is the least absolute deviations (LAD) procedure. Although LS is more widely used, the LAD approach yields better estimates in the presence of outliers. In this paper, we examine the performance of LAD estimators for the parameters of the first‐order autoregressive model in the presence of outliers. A simulation study compared these estimates with those given by LS. The general conclusion is that LAD does not deal successfully with additive outliers. A simple procedure is proposed which allows exception reporting when outliers occur.