USE OF MACHINE LEARNING METHODS IN FORECASTING INDICATORS OF FISCAL AND MONETARY POLICY COORDINATION FOR THE ECONOMY OF UZBEKISTAN

dc.contributor.author	Hakimov Hakimjon
dc.date.accessioned	2025-12-29T11:16:47Z
dc.date.issued	2024-01-06
dc.description.abstract	This paper proposes a new type of solution for Uzbekistan economy using several machine learning methods: LASSO, Ridge, Random Forest, Gradient Boosting and Artificial Neural Networks. This paper is one of the first attempts to apply machine learning methods to the macroeconomic forecasting in Uzbekistan. The main result of this paper is the confirmation of the possibility of more accurate forecasting of economic indicators in Uzbekistan using machine learning methods.
dc.format	application/pdf
dc.identifier.uri	https://americanjournal.org/index.php/ajbmeb/article/view/1717
dc.identifier.uri	https://asianeducationindex.com/handle/123456789/16438
dc.language.iso	eng
dc.publisher	American Journals
dc.relation	https://americanjournal.org/index.php/ajbmeb/article/view/1717/1590
dc.rights	https://creativecommons.org/licenses/by-nc/4.0
dc.source	American Journal of Business Management, Economics and Banking; Vol. 20 (2023); 36-44
dc.source	2832-8078
dc.subject	Macroeconomic indicators, ARMA, RMSE, LASSO, economic activity, random forest.
dc.title	USE OF MACHINE LEARNING METHODS IN FORECASTING INDICATORS OF FISCAL AND MONETARY POLICY COORDINATION FOR THE ECONOMY OF UZBEKISTAN
dc.type	info:eu-repo/semantics/article
dc.type	info:eu-repo/semantics/publishedVersion
dc.type	Peer-reviewed Article

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