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.

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.