Robust Estimators in Econometrics
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.
KeywordsBreakdown point Data contamination Generalized method of moments Heteroscedasticity Influence function Instrumental variables Least squares Linear regression Maximum likelihood Maximum-bias curve Measurement errors Mestimation Newton–Raphson procedure Nonlinear models Probability distribution Qualitative and quantitative robustness Quantile regression Random variables Robust econometrics Robust statistics S-estimation Simultaneous equations models Timeseries analysis
This work was supported by the Deutsche Forschungsgemeinschaft through the SFB 649 ‘Economic Risk’.
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