Home  Journals  Economic Analysis: Theory and Practice  3(570) - 2026 March

Evaluation of the key rate transmission mechanism in strategic sectors of the economy using artificial neural networks

Received: 6 February 2026

Accepted: 1 March 2026

Available online: 30 March 2026

Subject Heading: COMPREHENSIVE ECONOMIC AND FINANCIAL ANALYSIS

JEL Classification: С45, Е27, Е52, Е58, L16

Pages: 73-84

https://doi.org/10.24891/fmvuna

Subject. Analysis of the transmission mechanism of monetary policy in strategic sectors of the Russian economy, where traditional linear models often fail to describe complex, nonlinear reactions to key rate changes.
Objectives. To quantify and compare the sensitivity of strategic industries (fuel and energy complex, metallurgy, defense industry, agriculture) to changes in the key rate of the Bank of Russia, revealing heterogeneity in speed, strength and nature of transmission.
Methods. The methodological basis is a hybrid approach combining panel regression with a multilayer perceptron architecture. The neural network model allows you to capture complex nonlinear relationships that are inaccessible to traditional vector autoregressions. The empirical base covers quarterly observations for 2015–2025, including the dynamics of interest rates, industry indices of industrial production, the volume of investments in fixed assets and the weighted average cost of credit resources. A comparative assessment of the forecast accuracy was carried out relative to the basic VAR specification.
Results. Empirical results revealed a pronounced differentiation of transmission effects. The fuel and energy complex has demonstrated resilience to monetary shifts due to the dominance of the export environment and weak dependence on domestic lending. On the contrary, manufacturing industries in the defense industry segment turned out to be the most sensitive to changes in the value of money, but their reaction was clearly nonlinear: the restraining effect of a rate increase in recessionary periods exceeded the stimulating effect of its reduction in the recovery phase. The neural network specification reduced the standard error of the forecast by 18% relative to the linear alternative.
Conclusions. The results of the study refute the hypothesis of the homogeneity of strategic industries as an object of monetary influence. To improve the accuracy of forward-looking estimates, it is advisable for the Bank of Russia to differentiate transmission channels when modeling the consequences of key rate decisions, which will potentially enhance the effectiveness of monetary policy in the context of structural fragmentation of the economy.

Keywords: transmission mechanism, key rate, strategic industries, artificial neural network, monetary policy

References:

1. Bernanke B.S., Gertler M. Inside the Black Box: The Credit Channel of Monetary Policy Transmission. NBER Working Paper 5146, 1995. DOI: 10.3386/w5146
2. Mishkin F.S. The Channels of Monetary Transmission: Lessons for Monetary Policy. NBER Working Paper 5464, 1996. DOI: 10.3386/w5464
3. Byoung Jo Chun. Identifying Interest Rate Transmission Mechanism under a Bayesian Network. Sustainability, 2024, vol. 16, no. 14. DOI: 10.3390/su16145840
4. Glaz'ev S.Yu., Sukharev O.S., Afanas'eva O.N. [Monetary policy in Russia: negative cumulative effect in the framework of the neoclassical model and its overcoming]. Mikroekonomika, 2022, no. 2, pp. 5–38. (In Russ.) DOI: 10.33917/mic-2.103.2022.5-38 EDN: ULHQVE
5. Egorov A.V., Borzykh O.A.. [Symmetric interest rate passthrough in Russia]. Ekonomicheskaya politika, 2018, vol. 13, no. 1, pp. 92–121. (In Russ.) EDN: KZVTQD
6. Rasteryaeva T.V., Proshin D.D. [The impact of the key rate on the investment activity of market participants]. Finansovye rynki i banki, 2023, no. 8, pp. 59–64. (In Russ.) EDN: HCRMSU
7. Ponomarenko A.A. [Analysis of the monetary policy of the Russian Federation: inhibition by stability]. Ekonomika: vchera, segodnya, zavtra, 2024, vol. 14, no. 2A, pp. 351–360. (In Russ.) DOI: 10.34670/AR.2024.60.11.042 EDN: LDOLPD
8. Semenova N.N., Eremina O.I. [The influence of monetary policy on economic growth in Russia: theoretical approaches and practical implementation]. Finansy i kredit, 2018, vol. 24, iss. 4, pp. 819–832. (In Russ.) DOI: 10.24891/fc.24.4.819 EDN: TJCZZJ
9. Uzdenova F.M., El'kanov R.Kh. [The impact of changes in the central bank's key rate on the economic performance of the Russian Federation]. Voprosy ekonomiki i prava, 2023, no. 186, pp. 40–44. (In Russ.) DOI: 10.14451/2.186.40 EDN: SXZMJO
10. Naumenkova S.V., Mishchenko V.I. [Impact of the monetary credit system and the politics of the central bank upon the economic growth stimulation]. Problemy sovremennoi ekonomiki, 2015, no. 2, pp. 168–175. (In Russ.) EDN: UFHFYN
11. Zayarnaya I.A., Fedorova M.A. [The problem of forming the company’s pricing policy in conditions of risk]. Vestnik Altaiskoi akademii ekonomiki i prava, 2024, no. 1, pp. 80–83. (In Russ.) DOI: 10.17513/vaael.3214 EDN: NHYSKD
12. Sizon Yu.E., Uglitskikh O.N., Grishkina V.V. [Analysis of the impact of the key rate on the financial stability of insurance companies]. Finansy i uchetnaya politika, 2024, no. 3, pp. 13–17. (In Russ.) EDN: REJGAG
13. Basko O.V. [The impact of the key rate on long-term economic growth]. Finansovye issledovaniya, 2024, vol. 25, no. 4, pp. 31–40. (In Russ.) DOI: 10.54220/finis.1991-0525.2024.85.4.003 EDN: OLBLRV
14. Tishin A. [Monetary policy surprises in Russia]. Den'gi i kredit, 2019, no. 4, pp. 48–70. (In Russ.) DOI: 10.31477/rjmf.201904.48 EDN: ENUQXO
15. Pil'nik N.P., Pospelov I.G., Radionov S.A. [On limits of the influence of the Bank of Russia key rate on indicators of the Russian banking system]. Problemy prognozirovaniya, 2020, no. 2, pp. 137–147. (In Russ.) EDN: AQBLGH
16. Bukreeva I.A., Perepelitsa A.M., Zaikov S.N. [Assessment of the impact of the key rate on the development of the Russian economy]. Uchenye zapiski Altaiskogo filiala Rossiiskoi akademii narodnogo khozyaistva i gosudarstvennoi sluzhby pri Prezidente Rossiiskoi Federatsii, 2024, no. 2, pp. 32–35. (In Russ.) EDN: ILWUTK
17. Lomakin N.A., Kuz'mina T.I., Maramygin M.S. et al. [Digital deep learning model for forecasting the key rate of the Central bank of the Russian Federation in volatility]. Mezhdunarodnaya ekonomika, 2025, vol. 22, no. 2, pp. 170–185. (In Russ.) DOI: 10.33920/vne-04-2502-05 EDN: YZVMTC
18. Pershina A.A., Arutyunyan A.G., Kosnikov S.N. [Application of neural networks in forecasting economic trends]. Regional'naya i otraslevaya ekonomika, 2023, no. 5, pp. 164–171. (In Russ.) EDN: VMGZVI
19. Shomakhov K.R. [Prospects for using artificial intelligence to forecast financial trends], Vestnik Evraziiskoi nauki, 2024, vol. 16, no. s1. (In Russ.) EDN: DMVLPY
20. Fernández J.A.F. Banking systems in the euro zone and transmission of monetary policy. Central Bank Review, 2024, vol. 24, iss. 1, no. 100148. DOI: 10.1016/j.cbrev.2024.100148

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