An Analysis of the Indicator Saturation Estimator as a Robust Regression Estimator *
An Analysis of the Indicator Saturation Estimator as a Robust Regression Estimator *
This chapter analyzes an algorithm suggested by Hendry (1999) for estimation in a regression with more regressors than observations, with the purpose of finding an estimator that is robust to outliers and structural breaks. This estimator is an example of a one-step M-estimator based on Huber's skip function. The asymptotic theory is derived in the situation where there are no outliers or structural breaks using empirical process techniques. Stationary processes, trend stationary autoregressions, and unit root processes are considered.
Keywords: empirical processes, Huber's skip, indicator saturation, M-estimator, outlier robustness, vector autoregressive process
Oxford Scholarship Online requires a subscription or purchase to access the full text of books within the service. Public users can however freely search the site and view the abstracts and keywords for each book and chapter.
Please, subscribe or login to access full text content.
If you think you should have access to this title, please contact your librarian.
To troubleshoot, please check our FAQs , and if you can't find the answer there, please contact us .
