ANALYSIS OF BIG DATA INFLUENCE IN ECONOMY USING DIGITAL TECHNOLOGIES
DOI:
https://doi.org/10.60078/3060-4842-2025-vol2-iss5-pp840-843Abstract
The rapid development of digital technologies has changed the way data is collected, stored, and analyzed. Big Data has created new opportunities and challenges for econometric research. This article discusses the integration of econometric methods with big data analysis, the methodological innovations needed, and the results of empirical economic research. The study also highlights modern tools and ideas that help econometricians manage the complexity and scale of large data sets while maintaining model accuracy and interpretability. It also highlights how the combination of traditional econometric thinking and computational methods can improve the quality and scope of economic analysis in the modern digital economy
Keywords:
big data bias size diversity speed regularization reduction methods LASSO regression Ridge regression Panel data Time Series Python R Julia Statsmodels scikit-learn data. table PySpark causal forestsReferences
Athey, S. (2018). The Impact of Machine Learning on Economics. In The Economics of Artificial Intelligence. University of Chicago Press.
Belloni, A., Chernozhukov, V., & Hansen, C. (2014). High-Dimensional Methods and Inference on Structural and Treatment Effects. Journal of Economic Perspectives, 28(2), 29–50.
Einav, L., & Levin, J. (2014). Economics in the Age of Big Data. Science, 346(6210).
Mullainathan, S., & Spiess, J. (2017). Machine Learning: An Applied Econometric Approach. Journal of Economic Perspectives, 31(2), 87–106.
Varian, H. R. (2014). Big Data: New Tricks for Econometrics. Journal of Economic Perspectives, 28(2), 3–28.
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