Robust Estimation with Discrete Explanatory Variables
AbstractThe 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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Bibliographic InfoPaper provided by EconWPA in its series Econometrics with number 0203001.
Length: 81 pages
Date of creation: 02 Mar 2002
Date of revision:
Note: Type of Document - Acrobat PDF; pages: 81 ; figures: included
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discrete explanatory variables; linear regression; robust statistics; least trimmed squares;
Other versions of this item:
- Pavel Cizek, 2001. "Robust Estimation with Discrete Explanatory Variables," CERGE-EI Working Papers wp183, The Center for Economic Research and Graduate Education - Economic Institute, Prague.
- Čížek, Pavel, 2002. "Robust estimation with discrete explanatory variables," SFB 373 Discussion Papers 2002,76, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
- C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General
- C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models
- C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
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- Franco Peracchi, 1988. "Robust M-Estimators," UCLA Economics Working Papers 477, UCLA Department of Economics.
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"General Trimmed Estimation: Robust Approach to Nonlinear and Limited Dependent Variable Models,"
2004-130, Tilburg University, Center for Economic Research.
- Čížek, Pavel, 2008. "General Trimmed Estimation: Robust Approach To Nonlinear And Limited Dependent Variable Models," Econometric Theory, Cambridge University Press, vol. 24(06), pages 1500-1529, December.
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- 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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