Robust Regression in Stata

14 Pages Posted: 27 Mar 2009

See all articles by Vincenzo Verardi

Vincenzo Verardi

FUNDP - University of Namur. CRED

Christophe Croux

KU Leuven - Faculty of Business and Economics (FEB)

Date Written: October 2008

Abstract

In regression analysis, the presence of outliers in the data set can strongly distort the classical least squares estimator and lead to unreliable results. To deal with this, several robust-to-outliers methods have been proposed in the statistical literature. In Stata, some of these methods are available through the commands rreg and qreg. Unfortunately, these methods only resist to some specific types of outliers and turn out to be ineffective under alternative scenarios. In this paper we present more effective robust estimators that we implemented in Stata. We also present a graphical tool that allows recognizing the type of existing outliers.

Keywords: S-estimators, MM-estimators, Outliers, Robustness

JEL Classification: C12, C21, C87

Suggested Citation

Verardi, Vincenzo and Croux, Christophe, Robust Regression in Stata (October 2008). Available at SSRN: https://ssrn.com/abstract=1369144 or http://dx.doi.org/10.2139/ssrn.1369144

Vincenzo Verardi (Contact Author)

FUNDP - University of Namur. CRED ( email )

8 Rempart de la Vierge
Namur, 5000
Belgium

Christophe Croux

KU Leuven - Faculty of Business and Economics (FEB) ( email )

Naamsestraat 69
Leuven, B-3000
Belgium

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