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Robust Estimation with Discrete Explanatory Variables

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Author Info

  • Pavel Cizek

    (Humboldt Unniversity; CERGE-EI)

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.

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File URL: http://128.118.178.162/eps/em/papers/0203/0203001.pdf
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Bibliographic Info

Paper provided by EconWPA in its series Econometrics with number 0203001.

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Length: 81 pages
Date of creation: 02 Mar 2002
Date of revision:
Handle: RePEc:wpa:wuwpem:0203001

Note: Type of Document - Acrobat PDF; pages: 81 ; figures: included
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Web page: http://128.118.178.162

Related research

Keywords: discrete explanatory variables; linear regression; robust statistics; least trimmed squares;

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References

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  1. Franco Peracchi, 1988. "Robust M-Estimators," UCLA Economics Working Papers 477, UCLA Department of Economics.
  2. repec:wop:humbsf:2001-25 is not listed on IDEAS
  3. Zaman, Asad & Rousseeuw, Peter J. & Orhan, Mehmet, 2001. "Econometric applications of high-breakdown robust regression techniques," Economics Letters, Elsevier, vol. 71(1), pages 1-8, April.
  4. White, Halbert, 1980. "Nonlinear Regression on Cross-Section Data," Econometrica, Econometric Society, vol. 48(3), pages 721-46, April.
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Citations

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Cited by:
  1. Cizek, P., 2007. "General Trimmed Estimation: Robust Approach to Nonlinear and Limited Dependent Variable Models (Replaces DP 2007-1)," Discussion Paper 2007-65, Tilburg University, Center for Economic Research.
  2. Cizek, P., 2004. "General Trimmed Estimation: Robust Approach to Nonlinear and Limited Dependent Variable Models," Discussion Paper 2004-130, Tilburg University, Center for Economic Research.
  3. Cizek, P., 2006. "Efficient Robust Estimation of Regression Models (Replaced by DP 2007-87)," Discussion Paper 2006-8, Tilburg University, Center for Economic Research.
  4. Cizek, P., 2007. "General Trimmed Estimation: Robust Approach to Nonlinear and Limited Dependent Variable Models (Replaced by DP 2007-65)," Discussion Paper 2007-1, Tilburg University, Center for Economic Research.

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