University of Piraeus. International Strategic Management Association
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.peer-reviewe
