Journal of Management
vol. 12, iss. 1, pp. 157-169
data in economics and their (robust) analysis
of Computer Science, Czech Academy of Sciences & Institute of
Theory and Automation, Czech Academy of Sciences, Czech Republic
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.
This Work is licensed under a Creative Commons Attribution 4.0 License.
high-dimensional data, dimensionality reduction, linear regression,
classification analysis, robustness.
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