Some Robust Approaches to Reducing the Complexity of Economic Data

0581699 - ÚI 2024 RIV CZ eng C - Conference Paper (international conference)
Kalina, Jan
Some Robust Approaches to Reducing the Complexity of Economic Data.
The 17th International Days of Statistics and Economics Conference Proceedings. Praha: Melandrium, 2023 - (Löster, T.; Pavelka, T.), s. 246-255. ISBN 978-80-87990-31-5.
[International Days of Statistics and Economics /17./. Praha (CZ), 07.09.2023-09.09.2023]
R&D Projects: GA ČR GA21-05325S
Institutional support: RVO:67985807
Keywords : dimensionality reduction * Big Data * variable selection * robustness * sparsity
OECD category: Statistics and probability
https://msed.vse.cz/msed_2023/article/687-Kalina-Jan-paper.pdf

The recent advent of complex (and potentially big) data in economics requires modern and effective tools for their analysis including tools for reducing the dimensionality (complexity) of the given data. This paper starts with recalling the importance of Big Data in economics and with characterizing the main categories of dimension reduction techniques. While there have already been numerous techniques for dimensionality reduction available, this work is interested in methods that are robust to the presence of outlying measurements (outliers) in the economic data. Particularly, methods based on implicit weighting assigned to individual observations are developed in this paper. As the main contribution, this paper proposes three novel robust methods of dimension reduction. One method is a dimension reduction within a robust regularized linear regression, namely a sparse version of the least weighted squares estimator. The other two methods are robust versions of feature extraction methods popular in econometrics: robust principal component analysis and robust factor analysis.
Permanent Link: https://hdl.handle.net/11104/0349888