• Table of Contents
    • Abstract
    • Keywords
    • Article
      • 1 Measures of robustness
        • 1.1 Formalities
        • 1.2 Qualitative robustness
        • 1.3 Quantitative robustness
      • 2 Estimation approaches
        • 2.1 M-estimators
        • 2.2 S-estimators
        • 2.3 τ-estimators
      • 3 Methods of robust econometrics
        • 3.1 Discrete variables
        • 3.2 Time series
        • 3.3 Multivariate regression
        • 3.4 General estimation principles
    • See Also
    • Bibliography
    • How to cite this article

robust estimators in econometrics

P. Čížek and W. Härdle
From The New Palgrave Dictionary of Economics, Second Edition, 2008
Edited by Steven N. Durlauf and Lawrence E. Blume
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Econometric data are often obtained under conditions that cannot be well controlled, and so partial departures from the model assumptions in use (data contamination) occur relatively frequently. To address this, we first introduce concepts of robust statistics for qualifying and quantifying sensitivity of estimation methods to data contamination as well as important approaches to robust estimation. Later, we discuss how robust estimation methods have been adapted to various areas of econometrics, including time series analysis and general GMM-based estimation.
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How to cite this article

Čížek, P. and W. Härdle. "robust estimators in econometrics." The New Palgrave Dictionary of Economics. Second Edition. Eds. Steven N. Durlauf and Lawrence E. Blume. Palgrave Macmillan, 2008. The New Palgrave Dictionary of Economics Online. Palgrave Macmillan. 17 January 2018 <http://www.dictionaryofeconomics.com/article?id=pde2008_R000254> doi:10.1057/9780230226203.1452

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