IDEAS home Printed from https://ideas.repec.org/p/rbp/wpaper/2011-019.html
   My bibliography  Save this paper

Wavelet-based Core Inflation Measures: Evidence from Peru

Author

Listed:
  • Lahura, Erick

    (Central Bank of Peru
    Universidad Catolica del Peru)

  • Vega, Marco

    (Central Bank of Peru
    Universidad Catolica del Peru)

Abstract

Under inflation targeting and other related monetary policy regimes, the identification of non-transitory in ation and forecasts about future inflation constitute key ingredients for monetary policy decisions. In practice, central banks perform these tasks using so-called "core inflation measures". In this paper we construct alternative core inflation measures using wavelet functions and multiresolution analysis (MRA), and then evaluate their relevance for monetary policy. The construction of wavelet-based core inflation measures (WIMs) is relatively new in the literature and their assessment has not been addressed formally, this paper being the first attempt to perform both tasks for the case of Peru. Another main contribution of this paper is that it proposes a VAR-based long-run criterion as an alternative criteria for evaluating core inflation measures. Evidence from Peru shows that WIMs are superior to official core inflation in terms of both the proposed criterion and forecast-based criteria.

Suggested Citation

  • Lahura, Erick & Vega, Marco, 2011. "Wavelet-based Core Inflation Measures: Evidence from Peru," Working Papers 2011-019, Banco Central de Reserva del Perú.
    • Handle: RePEc:rbp:wpaper:2011-019
    as

    Download full text from publisher

    File URL: https://www.bcrp.gob.pe/docs/Publicaciones/Documentos-de-Trabajo/2011/Documento-de-Trabajo-19-2011.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Christoph Schleicher, 2002. "An Introduction to Wavelets for Economists," Staff Working Papers 02-3, Bank of Canada.
    2. 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.
    3. Ribba, Antonio, 2003. "Permanent-transitory decompositions and traditional measures of core inflation," Economics Letters, Elsevier, vol. 81(1), pages 109-116, October.
    4. Baqaee, David, 2010. "Using wavelets to measure core inflation: The case of New Zealand," The North American Journal of Economics and Finance, Elsevier, vol. 21(3), pages 241-255, December.
    5. Armas, Adrián & Vallejos , Lucy & Vega, Marco, 2011. "Indicadores tendenciales de inflación y su relevancia como variables indicativas de política monetaria," Revista Estudios Económicos, Banco Central de Reserva del Perú, issue 20, pages 27-56.
    6. Lahura Serrano, Erick W., 2004. "La relación dinero-producto, brecha del producto e inflación subyacente: algunas aplicaciones de las funciones Wavelets," Revista Estudios Económicos, Banco Central de Reserva del Perú, issue 11.
    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. Erick Lahura & Marco Vega, 2011. "Evaluation of Wavelet-based Core Inflation Measures: Evidence from Peru," Documentos de Trabajo / Working Papers 2011-320, Departamento de Economía - Pontificia Universidad Católica del Perú.
    2. Cortés Espada Josué Fernando & Sámano Daniel & Gutiérrez Villanueva Rubí, 2019. "Dynamics of Mexican Inflation: A Wavelet Analysis," Working Papers 2019-17, Banco de México.
    3. repec:rza:wpaper:503 is not listed on IDEAS
    4. Baqaee, David, 2010. "Using wavelets to measure core inflation: The case of New Zealand," The North American Journal of Economics and Finance, Elsevier, vol. 21(3), pages 241-255, December.
    5. Stan Plessis & Gideon Rand & Kevin Kotzé, 2015. "Measuring Core Inflation in South Africa," South African Journal of Economics, Economic Society of South Africa, vol. 83(4), pages 527-548, December.
    6. 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.
    7. 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.
    8. Alex Contreras & Zenón Quispe & Fernando Regalado, 2017. "Real dollarization and monetary policy in Peru," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Statistical implications of the new financial landscape, volume 43, Bank for International Settlements.
    9. 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.
    10. 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.
    11. repec:hum:wpaper:sfb649dp2006-021 is not listed on IDEAS
    12. Antonio Ribba, 2012. "The federal funds rate and the conduction of the international orchestra," Economics Bulletin, AccessEcon, vol. 32(4), pages 2983-2990.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    20. 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.
    21. Patrick M. Crowley, 2007. "A Guide To Wavelets For Economists," Journal of Economic Surveys, Wiley Blackwell, vol. 21(2), pages 207-267, April.
    22. Long Hai Vo & Duc Hong Vo, 2020. "Modelling Australian Dollar Volatility at Multiple Horizons with High-Frequency Data," Risks, MDPI, vol. 8(3), pages 1-16, August.

    More about this item

    Keywords

    Core infl ation; wavelets; forecast; structural VAR;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:rbp:wpaper:2011-019. 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: Research Unit (email available below). General contact details of provider: https://edirc.repec.org/data/bcrgvpe.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.