High-breakdown point estimation of some regression models

Sunil Sapra ()

Applied Economics Letters, 2003, vol. 10, issue 14, 875-878

Abstract: Common econometric estimators such as least squares, least absolute deviations (LAD), instrumental variables, maximum likelihood, and semiparametric estimators are non-robust against data contamination. Despite the known superiority of high-breakdown point (HBP) estimators in such situations, the HBP estimators have rarely been used in economics. This article presents some applications of an HBP estimator called the S-estimator (Rousseeuw and Yohai, Robust and Nonlinear Time Series Analysis (Eds) W.H. Franke and R.D. Martin, Springer-Verlag, NY, pp. 256-72, 1984) to the estimation of a linear regression model and compares the results with those obtained by ordinary least squares (OLS) and LAD methods. It is found that significance of variables as well as signs of coefficient estimates can be quite different under HBP estimation than under OLS and LAD estimation.

Date: 2003
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DOI: 10.1080/1350485032000162820

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