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Bank lending channel in Russia: A TVP-FAVAR approach

Author

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  • Borzykh, Olga

    (Central Bank of the Russian Federation (Bank of Russia), Moscow, Russian Federation)

Abstract

Implementation of inflation targeting by the Bank of Russia depends on the effectiveness of the channels of monetary policy transmission mechanism. In this article we use a TVP-FAVAR model to examine the bank lending channel. This channel describes the connection between monetary policy impulses and the amount of bank loans that are among the main sources of investments in Russia. In order to answer the main question we analyze the connection between the amount of bank loans and the money market interest rate MIACR which is the main operational target of the Bank of Russia. The use of TVP-FAVAR model allowed to solve a problem of omitted variables and to take into account gradual changes which occurred in the Russian economy during the period from January 2004 to December 2015. It is shown that the bank lending channel works only through the group of big banks that does not include biggest state-owned banks.

Suggested Citation

  • Borzykh, Olga, 2016. "Bank lending channel in Russia: A TVP-FAVAR approach," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 43, pages 96-117.
  • Handle: RePEc:ris:apltrx:0299
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    References listed on IDEAS

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    1. Tuuli Juurikkala & Alexei Karas & Laura Solanko, 2011. "The Role of Banks in Monetary Policy Transmission: Empirical Evidence from Russia," Review of International Economics, Wiley Blackwell, vol. 19(1), pages 109-121, February.
    2. Dimitris Korobilis, 2013. "Assessing the Transmission of Monetary Policy Using Time-varying Parameter Dynamic Factor Models-super-," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 75(2), pages 157-179, April.
    3. Lomivorotov, Rodion, 2015. "Bayesian estimation of monetary policy in Russia," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 38(2), pages 41-63.
    4. Deryugina, Elena B. & Ponomarenko, Alexey A., 2011. "Identifying structural shocks behind loan supply fluctuations in Russia," BOFIT Discussion Papers 20/2011, Bank of Finland, Institute for Economies in Transition.
    5. Timothy Cogley & Thomas J. Sargent, 2005. "Drift and Volatilities: Monetary Policies and Outcomes in the Post WWII U.S," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 8(2), pages 262-302, April.
    6. Ben S. Bernanke & Jean Boivin & Piotr Eliasz, 2005. "Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach," The Quarterly Journal of Economics, Oxford University Press, vol. 120(1), pages 387-422.
    7. Colin Ellis & Haroon Mumtaz & Pawel Zabczyk, 2014. "What Lies Beneath? A Time‐varying FAVAR Model for the UK Transmission Mechanism," Economic Journal, Royal Economic Society, vol. 0(576), pages 668-699, May.
    8. Sergey Drobyshevsky & Pavel Trunin & M. Kamenskikh, 2008. "Analysis of Transmission Mechanisms of Money and Credit Policy in Russia's Economy," Research Paper Series, Gaidar Institute for Economic Policy, issue 116P.
    9. Giorgio E. Primiceri, 2005. "Time Varying Structural Vector Autoregressions and Monetary Policy," Review of Economic Studies, Oxford University Press, vol. 72(3), pages 821-852.
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    Cited by:

    1. Silvo Dajčman & Alenka Kavkler & Sergey Merzlyakov & Sergey E. Pekarski & Dejan Romih, 2022. "International Transmission of Conventional and Unconventional Monetary Policy and Financial Stress Shocks from the Euro Area to Russia," Journal of Central Banking Theory and Practice, Central bank of Montenegro, vol. 11(1), pages 227-247.
    2. Ono, Shigeki, 2021. "The effects of monetary policy in Russia: A factor-augmented VAR approach," Economic Systems, Elsevier, vol. 45(3).
    3. Filipp Prokopev, 2021. "Balance Sheet Channel of Monetary Policy Evidence from Credit Spreads of Russian Firms," Russian Journal of Money and Finance, Bank of Russia, vol. 80(4), pages 3-30, December.
    4. Kurovskiy, Gleb, 2019. "Disentanglement of natural interest rate shocks and monetary policy shocks nexus," MPRA Paper 97547, University Library of Munich, Germany.
    5. Kurovskiy, Gleb, 2020. "Disentanglement of natural interest rate shocks and monetary policy shocks nexus," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 59, pages 128-143.
    6. Pestova, Anna, 2020. "“Credit view” on monetary policy in Russia," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 57, pages 72-88.
    7. Pestova, Anna A. (Пестова, Анна) & Mamonov, Mikhail E. (Мамонов, Михаил) & Rostova, Natalia A. (Ростова, Наталья), 2019. "Monetary Policy Shocks in the Russian Economy and Their Macroeconomic Effects [Шоки Процентной Политики Банка России И Оценка Их Макроэкономических Эффектов]," Economic Policy, Russian Presidential Academy of National Economy and Public Administration, vol. 4, pages 48-75, August.
    8. O. Borzykh., 2017. "The impact of banks’ capital adequacy ratio on bank lending channel of monetary transmission in Russia," VOPROSY ECONOMIKI, N.P. Redaktsiya zhurnala "Voprosy Economiki", vol. 7.
    9. Aizhan Bolatbayeva & Alisher Tolepbergen & Nurdaulet Abilov, 2020. "A macroeconometric model for Russia," Russian Journal of Economics, ARPHA Platform, vol. 6(2), pages 114-143, June.
    10. Irina Semina, 2020. "Modelling the Risk-taking Channel of Monetary Policy in the Russian Economy," Russian Journal of Money and Finance, Bank of Russia, vol. 79(3), pages 30-57, September.

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    More about this item

    Keywords

    monetary policy; bank lending channel; the Russian banks; groups of banks; TVP-FAVAR approach;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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