Outlier Detection in Regression Models with ARIMA Errors Using Robust Estimates

Bianco, Ana Maria, et al

Journal of Forecasting, 2001, vol. 20, issue 8, 565-79

Abstract: 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 by John Wiley & Sons, Ltd.

Date: 2001
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Persistent link: https://EconPapers.repec.org/RePEc:jof:jforec:v:20:y:2001:i:8:p:565-79

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