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Robust Estimation in Vector Autoregressive Moving‐Average Models
Author(s) -
Ben Marta Garcia,
Martinez Elena J.,
Yohai Victor J.
Publication year - 1999
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/1467-9892.00144
Subject(s) - mathematics , autoregressive model , autoregressive–moving average model , covariance matrix , estimation of covariance matrices , scatter matrix , outlier , statistics , goodness of fit , covariance , asymptotic distribution , monte carlo method , affine transformation , estimator , pure mathematics
Bustos and Yohai proposed a class of robust estimates for autoregressive moving‐average (ARMA) models based on residual autocovariances (RA estimates). In this paper an affine equivariant generalization of the RA estimates for vector ARMA processes is given. These estimates are asymptotically normal and, when the innovations have an elliptical distribution, their asymptotic covariance matrix differs only by a scalar factor from the covariance matrix corresponding to the maximum likelihood estimate. A Monte Carlo study confirms that the RA estimates are efficient under normal errors and robust when the sample contains outliers. A robust multivariate goodness‐of‐fit test based on the RA estimates is also obtained.