Please use this identifier to cite or link to this item:
|Title:||Robust estimators of ar-models : a comparison|
|Authors:||Donatos, George S.|
Meintanis, Simos G.
|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.|
|Appears in Collections:||European Research Studies Journal, Volume 1, Issue 1|
Files in This Item:
|Robust_estimators_of_Ar-models_a_comparison_1998.pdf||726.08 kB||Adobe PDF||View/Open|
Items in OAR@UM are protected by copyright, with all rights reserved, unless otherwise indicated.