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Title: Robust estimators of ar-models : a comparison
Authors: Donatos, George S.
Meintanis, Simos G.
Keywords: Regression analysis
Autoregression (Statistics)
Least squares
Issue Date: 1998
Publisher: University of Piraeus. International Strategic Management Association
Citation: Donatos, G. S., & Meintanis, S. G. (1998). Robust estimators of ar-models : a comparison. European Research Studies Journal, 1(1), 27-48.
Abstract: Many regression-estimation techniques have been extended to cover the case of dependent observations. The majority of such techniques are developed from the classical least squares, M and GM approaches and their properties have been investigated both on theoretical and empirical grounds. However, the behavior of some alternative methods- with satisfactory performance in the regression case- has not received equal attention in the context of time series. A simulation study of four robust estimators for autoregressive models containing innovation or additive outliers is presented. The robustness and efficiency properties of the methods are exhibited, some finite-sample results are discussed in combination with theoretical properties and the relative merits of the estimators are viewed in connection with the outlier-generating scheme.
ISSN: 11082976
Appears in Collections:European Research Studies Journal, Volume 1, Issue 1

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