The Bulletin of the Institute of Economics of the Russian Academy of Sciences № 5/2025. Economics and Management.

Vladimir L. Bersenev

Dr. Sci. (Hist.), Professor, Leading Researcher, Institute of Economics of the Urals Branch of the RAS, Yekaterinburg, Russia

ORCID: 0000-0002-3554-6965

Olga N. Buchinskaia

Cand. Sci. (Econ.), Senior Researcher, Institute of Economics of the Urals Branch of the RAS, Yekaterinburg, Russia

ORCID: 0000-0002- 5421-2522

ANALYSIS OF THREATS TO THE RESILIENCE OF THE RUSSIAN ECONOMY USING MACHINE LEARNING ALGORITHMS

121-145

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The increasing number of exogenous and endogenous shocks faced by national economies, as well as various measures of external negative impact on the country’s economy, necessitate a diagnosis of threats to the viability of the economy. Based on the statistical data of the Russian Federation for the period 1990–2023, eight different types of shocks were identified, which were classified as external or internal, natural or artificial in origin. Some of these became bifurcation points, changing the vector of Russian economic development. Since the impact of artificially induced shocks cannot be analyzed through deterministic models, it becomes necessary to use current statistical data instead of analyzing long time series to diagnose threats to the resilience of the Russian economy. To this end, the authors used machine learning algorithms, including identifying specific machine learning algorithms that allow for a fairly accurate diagnosis of the impact of shocks on the resilience of our country’s economic system. The AdaBoost and decision tree algorithms demonstrated the best results. Machine learning was used to identify the variables the algorithms selected for shock impact diagnostics, as well as to determine the threshold values for the indicators used by the algorithms to diagnose economic shocks.

Keywords: economic shocks, economic crises, resilience, economic stability, economic diagnostics, machine learning.

JEL: E37

EDN: CBAPHB

DOI: https://doi.org/10.52180/2073-6487_2025_5_121_145

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Manuscript submission date: 30.05.2025

Manuscript acceptance date: 13.10.2025

For citation:

Bersenev V.L., Buchinskaia O.N. Analysis of threats to the resilience of the Russian economy using machine learning algorithms // Vestnik Instituta Ekonomiki Rossiyskoy Akademii Nauk. 2025. № 5. Pp. 121-145. (In Russ.). https://doi.org/10.52180/2073-6487_2025_5_121_145 EDN: CBAPHB

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