Robust estimation for ARMA models
Statistics Theory
2009-04-02 v1 Statistics Theory
Abstract
This paper introduces a new class of robust estimates for ARMA models. They are M-estimates, but the residuals are computed so the effect of one outlier is limited to the period where it occurs. These estimates are closely related to those based on a robust filter, but they have two important advantages: they are consistent and the asymptotic theory is tractable. We perform a Monte Carlo where we show that these estimates compare favorably with respect to standard M-estimates and to estimates based on a diagnostic procedure.
Cite
@article{arxiv.0904.0106,
title = {Robust estimation for ARMA models},
author = {Nora Muler and Daniel Peña and Víctor J. Yohai},
journal= {arXiv preprint arXiv:0904.0106},
year = {2009}
}
Comments
Published in at http://dx.doi.org/10.1214/07-AOS570 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)