Before implementing any multivariate statistical analysis based on em- pirical covariance matrices, it is important to check whether outliers are present because their existence could induce significant biases. In this article, we present the minimum covariance determinant estimator, which is...

The analysis of the empirical distribution of univariate data often includes the computation of location, scale, skewness, and tail-heaviness measures, which are estimates of specific parameters of the underlying population distribution. Several measures are available, but they differ by...

In this article, we introduce a new Stata command, smultiv, that implements the S-estimator of multivariate location and scatter. Using simulated data, we show that smultiv outperforms mcd, an alternative robust estimator. Finally, we use smultiv to perform robust principal component analysis...

In this article, we describe http://www.stata-journal.com/software/Robinson’s (1988, Econometrica 56: 931– 954) double residual semiparametric regression estimator and H ̈ardle and Mam- men’s (1993, Annals of Statistics 21: 1926–1947) specification test implementation in Stata. We use...

In regression analysis, the presence of outliers in the dataset can strongly distort the classical least-squares estimator 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...

In this article, we describe the Stata implementation of Baltagi and Li's (2002, Annals of Economics and Finance 3: 103–116) series estimator of partially linear panel-data models with fixed effects. After a brief description of the estimator itself, we describe the new command xtsemipar. We...

The classical instrumental-variables estimator is extremely sensitive to the presence of outliers in the sample. This is a concern because outliers can strongly distort the estimated effect of a given regressor on the dependent variable. Although outlier diagnostics exist, they frequently fail...