Outlier Detection in Regression Models with ARIMA Errors using Robust Estimates | Zendy

Bianco A. M. | Zendy; García Ben M. | Zendy; Martínez E. J. | Zendy; Yohai V. J. | Zendy

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Outlier Detection in Regression Models with ARIMA Errors using Robust Estimates

Author(s) -

Bianco A. M.,

García Ben M.,

Martínez E. J.,

Yohai V. J.

Publication year - 2001

Publication title -

journal of forecasting

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.543

H-Index - 59

eISSN - 1099-131X

pISSN - 0277-6693

DOI - 10.1002/for.768

Subject(s) - outlier , autoregressive integrated moving average , robust regression , monte carlo method , statistics , regression , kalman filter , econometrics , computer science , linear regression , anomaly detection , regression analysis , robust statistics , mathematics , time series , artificial intelligence

A diagnostic procedure for detecting additive and innovation outliers as well as level shifts in a regression model with ARIMA errors is introduced. The procedure is based on a robust estimate of the model parameters and on innovation residuals computed by means of robust filtering. A Monte Carlo study shows that, when there is a large proportion of outliers, this procedure is more powerful than the classical methods based on maximum likelihood type estimates and Kalman filtering. Copyright © 2001 John Wiley & Sons, Ltd.

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