Robust regression in Stata
AbstractIn regression analysis, the presence of outliers in the dataset 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 rreg and qreg commands. Unfortunately, these methods resist only some specific types of outliers and turn out to be ineffective under alternative scenarios. In this article, we present more effective robust estimators that we implemented in Stata. We also present a graphical tool that recognizes the type of detected outliers.
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Bibliographic InfoArticle provided by StataCorp LP in its journal Stata Journal.
Volume (Year): 9 (2009)
Issue (Month): 3 (September)
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Web page: http://www.stata-journal.com/
Other versions of this item:
- Verardi, Vincenzo & Croux, Christophe, 2008. "Robust regression in Stata," Open Access publications from Katholieke Universiteit Leuven urn:hdl:123456789/202142, Katholieke Universiteit Leuven.
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Croux, Christophe & Dhaene, Geert & Hoorelbeke, Dirk, 2004.
"Robust standard errors for robust estimators,"
Open Access publications from Katholieke Universiteit Leuven
urn:hdl:123456789/238634, Katholieke Universiteit Leuven.
- Croux, Christophe & Dhaene, Geert & Hoorelbeke, Dirk, 2003. "Robust standard errors for robust estimators," Open Access publications from Katholieke Universiteit Leuven urn:hdl:123456789/118271, Katholieke Universiteit Leuven.
- Christophe Croux & Geert Dhaene & Dirk Hoorelbeke, 2003. "Robust Standard Errors for Robust Estimators," Center for Economic Studies - Discussion papers ces0316, Katholieke Universiteit Leuven, Centrum voor Economische Studiën.
- Maria Caterina Bramati & Christophe Croux, 2007. "Robust estimators for the fixed effects panel data model," Econometrics Journal, Royal Economic Society, vol. 10(3), pages 521-540, November.
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