Robust Estimation with Discrete Explanatory Variables

Pavel Cizek

Econometrics from University Library of Munich, Germany

Abstract: The least squares estimator is probably the most frequently used estimation method in regression analysis. Unfortunately, it is also quite sensitive to data contamination and model misspecification. Although there are several robust estimators designed for parametric regression models that can be used in place of least squares, these robust estimators cannot be easily applied to models containing binary and categorical explanatory variables. Therefore, I design a robust estimator that can be used for any linear regression model no matter what kind of explanatory variables the model contains. Additionally, I propose an adaptive procedure that maximizes the efficiency of the proposed estimator for a given data set while preserving its robustness.

Keywords: discrete explanatory variables; linear regression; robust statistics; least trimmed squares (search for similar items in EconPapers)
JEL-codes: C20 C24 C61 (search for similar items in EconPapers)
Pages: 81 pages
Date: 2002-03-02
New Economics Papers: this item is included in nep-ecm
Note: Type of Document - Acrobat PDF; pages: 81 ; figures: included
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https://econwpa.ub.uni-muenchen.de/econ-wp/em/papers/0203/0203001.pdf (application/pdf)

Related works:
Working Paper: Robust Estimation with Discrete Explanatory Variables (2001)
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Persistent link: https://EconPapers.repec.org/RePEc:wpa:wuwpem:0203001

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