Robust Estimators of Ar-Models: A Comparison

G.S. Donatos and S.G. Meintanis

European Research Studies Journal, 1998, vol. I, issue 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.

Keywords: Robust estimation; Simulation; Innovation and additive outliers; Least median of squares; Functional least squares (search for similar items in EconPapers)
Date: 1998
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Persistent link: https://EconPapers.repec.org/RePEc:ers:journl:v:i:y:1998:i:1:p:27-48

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