Outlier Detection in Regression Models with ARIMA Errors using Robust Estimates

✍ Scribed by A. M. Bianco; M. García Ben; E. J. Martínez; V. J. Yohai

• Publisher: John Wiley and Sons
• Year: 2001
• Tongue: English
• Weight: 130 KB
• Volume: 20
• Category: Article
• ISSN: 0277-6693
• DOI: 10.1002/for.768

✦ Synopsis

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 John Wiley & Sons, Ltd.