Classical Machine Learning Methods in Economics Research: Macro and Micro Level Examples

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Abstract: Paper reviews the classical methods of machine learning (supervised and unsupervised learning), gives examples of the application of different methods and discusses approaches that will be useful for empirical economics research (on data from Ukrainian firms, banks and official state statistics). The different sectors of economics are investigated: the multiple linear regression is used on macrolevel for macro production function of Ukraine specification; logistic regression is used in bank sector for credit risk management with the scoring model; k-means, hierarchic clustering and DBSCAN are used in regional level for regions of Ukraine grouping based on competitiveness; principal component analysis is used for firm’s financial stability analysis. All models showed adequate simulation results according to the quality criteria of the models. So, the possibility of classic machine learning methods application for investigations of the processes and objects on different levels of economics (micro, mezzo and macro) is demonstrated in the article

WSEAS Transactions on Business and Economics, ISSN / E-ISSN: 1109-9526 / 2224-2899, Volume 18, 2021, Art. #22

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Vitalina Babenko, Andriy Panchyshyn, Larysa Zomchak, Maryna Nehrey, Zoriana Artym-drohomyretska, Taras Lahotskyi, "Classical Machine Learning Methods in Economics Research: Macro and Micro Level Examples," WSEAS Transactions on Business and Economics, vol. 18, pp. 209-217, 2021, DOI:10.37394/23207.2021.18.22
Vitalina Babenko, Andriy Panchyshyn, Larysa Zomchak, Maryna Nehrey, Zoriana Artym-drohomyretska, Taras Lahotskyi. Classical Machine Learning Methods in Economics Research: Macro and Micro Level Examples. WSEAS Transactions on Business and Economics. 2021;18:209-217. 10.37394/23207.2021.18.22
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