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Using Wavelets to Analyse the Dynamics of Inflation Processes

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  • Mikhail Starichkov

    (Bank of Russia)

Abstract

This paper proposes the use of wavelet analysis as an additional tool for studying inflation data. The corresponding mathematical apparatus is actively used in various fields and has proven effective for working with non-stationary signals due to its informativeness, clarity, and adaptability to the study of local features. Wavelets scan the observed series in a two-dimensional space in frequency and time, allowing to determine how significantly and at what specific moment certain groups of frequency components manifest themselves and when significant changes in data behaviour occur. This enables a multiscale analysis of the dynamics of the process under study. This is particularly relevant because, while jumps in data are usually very noticeable, interactions of events on small scales that develop into large-scale phenomena are much more difficult to detect. Conversely, focusing only on small details may result in missing phenomena occurring at the global level.

Suggested Citation

  • Mikhail Starichkov, 2025. "Using Wavelets to Analyse the Dynamics of Inflation Processes," Russian Journal of Money and Finance, Bank of Russia, vol. 84(1), pages 105-128, March.
    • Handle: RePEc:bkr:journl:v:84:y:2025:i:1:p:105-128
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    References listed on IDEAS

    as
    1. Christoph Schleicher, 2002. "An Introduction to Wavelets for Economists," Staff Working Papers 02-3, Bank of Canada.
    2. Crowley, Patrick M. & Hudgins, David, 2017. "Wavelet-based monetary and fiscal policy in the Euro area," Journal of Policy Modeling, Elsevier, vol. 39(2), pages 206-231.
    3. Anna Brychykova & Elena Mogilevich & Alexey Shvedov, 2019. "On Wavelet Transform for Stock Price Modeling by Fuzzy Systems," HSE Economic Journal, National Research University Higher School of Economics, vol. 23(3), pages 444-464.
    4. Winkelmann, Lars, 2013. "Quantitative forward guidance and the predictability of monetary policy: A wavelet based jump detection approach," SFB 649 Discussion Papers 2013-016, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    5. Patrick M. Crowley & David Hudgins, 2022. "Monetary policy objectives and economic outcomes: What can we learn from a wavelet‐based optimal control approach?," Manchester School, University of Manchester, vol. 90(2), pages 144-170, March.
    Full references (including those not matched with items on IDEAS)

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    Keywords

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    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C65 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Miscellaneous Mathematical Tools
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation

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