This work is devoted to statistical methods for the analysis of economic data with a large number of variables. The authors present a review of references documenting that such data are more and more commonly available in various theoretical and applied economic problems and their analysis can be hardly performed with standard econometric methods. The paper is focused on highdimensional data, which have a small number of observations, and gives an overview of recently proposed methods for their analysis in the context of econometrics, particularly in the areas of dimensionality reduction, linear regression and classification analysis. Further, the performance of various methods is illustrated on a publicly available benchmark data set on credit scoring. In comparison with other authors, robust methods designed to be insensitive to the presence of outlying measurements are also used. Their strength is revealed after adding an artificial contamination by noise to the original data. In addition, the performance of various methods for a prior dimensionality reduction of the data is compared

中文翻译：

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经济学中的高维数据及其（稳健的）分析

这项工作致力于分析具有大量变量的经济数据的统计方法。作者对参考文献进行了回顾，这些文献证明此类数据在各种理论和应用经济问题中越来越普遍，并且很难用标准的计量经济学方法进行分析。该论文侧重于具有少量观察值的高维数据，并概述了最近提出的在计量经济学背景下对其进行分析的方法，特别是在降维、线性回归和分类分析领域。此外，在公开可用的信用评分基准数据集上说明了各种方法的性能。与其他作者相比，还使用了设计为对外围测量不敏感的稳健方法。在对原始数据添加人工噪声污染后，它们的强度就会显现出来。此外，还比较了用于数据先验降维的各种方法的性能