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Analysis of Inflation Risk Factors in Russia

Author

Listed:
  • Alexandra Chudaeva

    (Institute of Applied Economic Research, RANEPA)

Abstract

This paper uses quantile regression combined with the local projections approach to study the impact of macroeconomic and financial factors on the conditional distribution of inflation and inflation risk in Russia, i.e. the probability of high values of price growth. It is shown that the main predictors of growth in inflation risk are increases in nominal wages and retail turnover, the weakening of the Russian rouble, and declines in production. In addition, geopolitical tension and the narrowing of the bond spread could indicate the intensification of this risk. It is revealed that the effect of the pass-through of the dynamics of the exchange rate to consumer prices is manifested even more with growth in the inflation rate. At the same time, in a high inflation environment, the strengthening and weakening of the Russian rouble are passed through to prices with varying intensity, greater in the second case. A notable reduction of the risk of critically high inflation may require the central bank to take comprehensive measures to fight inflation, since the effect of a rising interest rate on inflation risk is less significant compared to the effect on the average value of price growth.

Suggested Citation

  • Alexandra Chudaeva, 2025. "Analysis of Inflation Risk Factors in Russia," Russian Journal of Money and Finance, Bank of Russia, vol. 84(1), pages 60-92, March.
    • Handle: RePEc:bkr:journl:v:84:y:2025:i:1:p:60-92
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    References listed on IDEAS

    as
    1. Dario Caldara & Matteo Iacoviello, 2022. "Measuring Geopolitical Risk," American Economic Review, American Economic Association, vol. 112(4), pages 1194-1225, April.
    2. Urmat Dzhunkeev, 2024. "Forecasting Inflation in Russia Using Gradient Boosting and Neural Networks," Russian Journal of Money and Finance, Bank of Russia, vol. 83(1), pages 53-76, March.
    3. Taylor, John B., 2000. "Low inflation, pass-through, and the pricing power of firms," European Economic Review, Elsevier, vol. 44(7), pages 1389-1408, June.
    4. Delatte, Anne-Laure & López-Villavicencio, Antonia, 2012. "Asymmetric exchange rate pass-through: Evidence from major countries," Journal of Macroeconomics, Elsevier, vol. 34(3), pages 833-844.
    5. Rossi, Barbara & Sekhposyan, Tatevik, 2019. "Alternative tests for correct specification of conditional predictive densities," Journal of Econometrics, Elsevier, vol. 208(2), pages 638-657.
    6. Goldberg, Linda S. & Campa, José Manuel, 2006. "Distribution Margins, Imported Inputs and the Insensitivity of the CPI to Exchange Rates," CEPR Discussion Papers 5650, C.E.P.R. Discussion Papers.
    7. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1593-1636.
    8. Mustafa Kocoglu, 2023. "Drivers of inflation in Turkey: a new Keynesian Phillips curve perspective," Economic Change and Restructuring, Springer, vol. 56(4), pages 2825-2853, August.
    9. Floden, Martin & Wilander, Fredrik, 2006. "State dependent pricing, invoicing currency, and exchange rate pass-through," Journal of International Economics, Elsevier, vol. 70(1), pages 178-196, September.
    10. Yoshibumi Makabe & Yoshihiko Norimasa, 2022. "The Term Structure of Inflation at Risk: A Panel Quantile Regression Approach," Bank of Japan Working Paper Series 22-E-4, Bank of Japan.
    11. Y. Ponomarev & P. Trunin & A. Ulyukayev., 2014. "Exchange Rate Pass-through in Russia," VOPROSY ECONOMIKI, N.P. Redaktsiya zhurnala "Voprosy Economiki", vol. 3.
    12. Y. Ponomarev & P. Trunin & A. Ulyukayev, 2014. "Exchange Rate Pass-through in Russia," Voprosy Ekonomiki, NP Voprosy Ekonomiki, issue 3.
    13. Banerjee, Ryan & Contreras, Juan & Mehrotra, Aaron & Zampolli, Fabrizio, 2024. "Inflation at risk in advanced and emerging market economies," Journal of International Money and Finance, Elsevier, vol. 142(C).
    14. Konstantin Styrin, 2019. "Forecasting Inflation in Russia Using Dynamic Model Averaging," Russian Journal of Money and Finance, Bank of Russia, vol. 78(1), pages 3-18, March.
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    More about this item

    Keywords

    inflation; inflation risk; risk assessment; quantile regression; conditional distribution; exchange rate pass-through effect; monetary policy;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • F31 - International Economics - - International Finance - - - Foreign Exchange

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