Premium
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.