IDEAS home Printed from https://ideas.repec.org/a/bkr/journl/v84y2025i1p105-128.html
   My bibliography  Save this article

Using Wavelets to Analyse the Dynamics of Inflation Processes

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://rjmf.econs.online/upload/iblock/3be/p4v30ueyi0l6ql8fxmepqto9isfujbd4/Using-Wavelets-to-Analyse-the-Dynamics-of-Inflation-Processes.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Christoph Schleicher, 2002. "An Introduction to Wavelets for Economists," Staff Working Papers 02-3, Bank of Canada.
    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. 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.
    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)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hudgins, David & Crowley, Patrick M., 2017. "Modelling a small open economy using a wavelet-based control model," Research Discussion Papers 32/2017, Bank of Finland.
    2. repec:zbw:bofrdp:2019_011 is not listed on IDEAS
    3. Lassaâd Mbarek & Hardik A. Marfatia & Sonja Juko, 2018. "Time-varying Response of Treasury Yields to Monetary Policy Shocks: Evidence from the Tunisian Bond Market," Working Papers 1243, Economic Research Forum, revised 23 Oct 2018.
    4. Yushu Li & Fredrik N. G. Andersson, 2021. "A simple wavelet-based test for serial correlation in panel data models," Empirical Economics, Springer, vol. 60(5), pages 2351-2363, May.
    5. Lahura, Erick & Vega, Marco, 2011. "Wavelet-based Core Inflation Measures: Evidence from Peru," Working Papers 2011-019, Banco Central de Reserva del Perú.
    6. Li, Yushu & Shukur, Ghazi, 2010. "Linear and Non-linear Causality Test in a LSTAR model - wavelet decomposition in a non-linear environment," Working Paper Series in Economics and Institutions of Innovation 227, Royal Institute of Technology, CESIS - Centre of Excellence for Science and Innovation Studies.
    7. Francis In & Sangbae Kim, 2012. "An Introduction to Wavelet Theory in Finance:A Wavelet Multiscale Approach," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 8431, September.
    8. Boubaker Heni & Canarella Giorgio & Gupta Rangan & Miller Stephen M., 2017. "Time-varying persistence of inflation: evidence from a wavelet-based approach," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 21(4), pages 1-18, September.
    9. Luca De Benedictis & Marco Gallegati, 2005. "Trade balance and terms of trade in U.S.: a time-scale decomposition analysis," International Trade 0512016, University Library of Munich, Germany.
    10. Usman Khalid & Olivier Habimana, 2021. "Military Spending and Economic Growth in Turkey: A Wavelet Approach," Defence and Peace Economics, Taylor & Francis Journals, vol. 32(3), pages 362-376, April.
    11. Thabani Ndlovu & Delson Chikobvu, 2023. "A Wavelet-Decomposed WD-ARMA-GARCH-EVT Model Approach to Comparing the Riskiness of the BitCoin and South African Rand Exchange Rates," Data, MDPI, vol. 8(7), pages 1-24, July.
    12. Neeraj, & Panigrahi, Prasanta K., 2017. "Causality and correlations between BSE and NYSE indexes: A Janus faced relationship," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 481(C), pages 284-313.
    13. Ibrahim Ahamada & Philippe Jolivaldt, 2010. "Classical vs wavelet-based filters Comparative study and application to business cycle," Documents de travail du Centre d'Economie de la Sorbonne 10027, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    14. Hudgins, David & Crowley, Patrick M., 2017. "Modelling a small open economy using a wavelet-based control model," Bank of Finland Research Discussion Papers 32/2017, Bank of Finland.
    15. Muhammad Azmat Hayat & Huma Ghulam & Maryam Batool & Muhammad Zahid Naeem & Abdullah Ejaz & Cristi Spulbar & Ramona Birau, 2021. "Investigating the Causal Linkages among Inflation, Interest Rate, and Economic Growth in Pakistan under the Influence of COVID-19 Pandemic: A Wavelet Transformation Approach," JRFM, MDPI, vol. 14(6), pages 1-22, June.
    16. Bera, Anil Kumar & Uyar, Umut & Kangalli Uyar, Sinem Guler, 2020. "Analysis of the five-factor asset pricing model with wavelet multiscaling approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 76(C), pages 414-423.
    17. Ozun, Alper & Cifter, Atilla, 2007. "Modeling Long-Term Memory Effect in Stock Prices: A Comparative Analysis with GPH Test and Daubechies Wavelets," MPRA Paper 2481, University Library of Munich, Germany.
    18. Patrick M. Crowley, 2007. "A Guide To Wavelets For Economists," Journal of Economic Surveys, Wiley Blackwell, vol. 21(2), pages 207-267, April.
    19. Zsuzsanna Hosszu & Gergely Lakos, 2022. "Early Warning Performance of Univariate Credit-to-GDP Gaps," MNB Occasional Papers 2022/142, Magyar Nemzeti Bank (Central Bank of Hungary).
    20. repec:hum:wpaper:sfb649dp2014-027 is not listed on IDEAS
    21. Lehkonen, Heikki & Heimonen, Kari, 2014. "Timescale-dependent stock market comovement: BRICs vs. developed markets," Journal of Empirical Finance, Elsevier, vol. 28(C), pages 90-103.
    22. Dowd, Kevin & Cotter, John & Loh, Lixia, 2011. "U.S. Core Inflation: A Wavelet Analysis," Macroeconomic Dynamics, Cambridge University Press, vol. 15(4), pages 513-536, September.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bkr:journl:v:84:y:2025:i:1:p:105-128. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Olga Kuvshinova (email available below). General contact details of provider: https://edirc.repec.org/data/cbrgvru.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.