In regression and multivariate analysis, the presence of outliers in the dataset can strongly distort classical estimations and lead to unreliable results. To deal with this, several robust-to-outliers methods have been proposed in the statistical literature. In Stata, some of these methods are available through the commands rreg and qreg for robust regression and hadimvo for multivariate outliers identification. Unfortunately, these methods only resist some specific types of outliers and turn out to be ineffective under alternative scenarios. In this presentation, after illustrating the drawbacks of the available methods, we present more effective robust estimators that we implemented in Stata. We also present a graphical tool that allows users to recognize the type of existing outliers in regression analysis.
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