IDEAS home Printed from https://ideas.repec.org/p/bca/bocawp/02-3.html

An Introduction to Wavelets for Economists

Author

Listed:
  • Christoph Schleicher

Abstract

Wavelets are mathematical expansions that transform data from the time domain into different layers of frequency levels. Compared to standard Fourier analysis, they have the advantage of being localized both in time and in the frequency domain, and enable the researcher to observe and analyze data at different scales. While their theoretical foundations were completed by the late 1980s, the 1990s saw a rapid spread to a wide range of applied sciences. A number of successful applications indicate that wavelets are on the verge of entering mainstream econometrics. This paper gives an informal and non-technical introduction to wavelets, and describes their potential for the economic researcher.

Suggested Citation

  • Christoph Schleicher, 2002. "An Introduction to Wavelets for Economists," Staff Working Papers 02-3, Bank of Canada.
    • Handle: RePEc:bca:bocawp:02-3
    DOI: 10.34989/swp-2002-3
    as

    Download full text from publisher

    File URL: https://doi.org/10.34989/swp-2002-3
    File Function: Abstract
    Download Restriction: no
    File URL: https://www.oar-rao.bank-banque-canada.ca/record/895/files/wp02-3.pdf
    File Function: Full text
    Download Restriction: no
    File URL: https://libkey.io/10.34989/swp-2002-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. John Geweke & Susan Porter‐Hudak, 1983. "The Estimation And Application Of Long Memory Time Series Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 4(4), pages 221-238, July.
    2. Michael H. Neumann, 1996. "Spectral Density Estimation Via Nonlinear Wavelet Methods For Stationary Non‐Gaussian Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 17(6), pages 601-633, November.
    3. Ramsey, James B. & Lampart, Camille, 1998. "Decomposition Of Economic Relationships By Timescale Using Wavelets," Macroeconomic Dynamics, Cambridge University Press, vol. 2(1), pages 49-71, March.
    4. Nason, G.P. & von Sachs, R., 1999. "Wavelets in Time Series Analysis," Papers 9901, Catholique de Louvain - Institut de statistique.
    5. Davidson, Russell & Labys, Walter C & Lesourd, Jean-Baptiste, 1998. "Wavelet Analysis of Commodity Price Behavior," Computational Economics, Springer;Society for Computational Economics, vol. 11(1-2), pages 103-128, April.
    6. Rainer Von Sachs & Brenda Macgibbon, 2000. "Non‐parametric Curve Estimation by Wavelet Thresholding with Locally Stationary Errors," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 27(3), pages 475-499, September.
    7. Mark J. Jensen, 1997. "Using Wavelets to Obtain a Consistent Ordinary Least Squares Estimator of the Long Memory Parameter," Econometrics 9710002, University Library of Munich, Germany.
    8. Hong, Yongmiao & Lee, Jin, 2001. "One-Sided Testing For Arch Effects Using Wavelets," Econometric Theory, Cambridge University Press, vol. 17(6), pages 1051-1081, December.
    9. Paul Conway & David Frame, 2000. "A spectral analysis of New Zealand output gaps using Fourier and wavelet techniques," Reserve Bank of New Zealand Discussion Paper Series DP2000/06, Reserve Bank of New Zealand.
    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. 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.
    2. Crowley, Patrick M., 2005. "An intuitive guide to wavelets for economists," Bank of Finland Research Discussion Papers 1/2005, Bank of Finland.
    3. Taylor, Larry W., 2009. "Using the Haar wavelet transform in the semiparametric specification of time series," Economic Modelling, Elsevier, vol. 26(2), pages 392-403, March.
    4. Patrick Crowley, 2005. "An intuitive guide to wavelets for economists," Econometrics 0503017, University Library of Munich, Germany.
    5. Fan, Yanqin & Gençay, Ramazan, 2010. "Unit Root Tests With Wavelets," Econometric Theory, Cambridge University Press, vol. 26(5), pages 1305-1331, October.
    6. SangKun Bae & Mark J. Jensen, 1998. "Long-Run Neutrality in a Long-Memory Model," Macroeconomics 9809006, University Library of Munich, Germany, revised 21 Apr 1999.
    7. Avishek Bhandari & Bandi Kamaiah, 2020. "Long memory in select stock returns using an alternative wavelet log-scale alignment approach," Papers 2004.08550, arXiv.org.
    8. Avishek Bhandari & Bandi Kamaiah, 2021. "Long Memory and Fractality Among Global Equity Markets: a Multivariate Wavelet Approach," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(1), pages 23-37, March.
    9. Christian M. Hafner, 2012. "Cross-correlating wavelet coefficients with applications to high-frequency financial time series," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(6), pages 1363-1379, December.
    10. Bhandari, Avishek, 2020. "Long Memory and Correlation Structures of Select Stock Returns Using Novel Wavelet and Fractal Connectivity Networks," MPRA Paper 101946, University Library of Munich, Germany.
    11. Patrick M. Crowley, 2007. "A Guide To Wavelets For Economists," Journal of Economic Surveys, Wiley Blackwell, vol. 21(2), pages 207-267, April.
    12. Yousefi, Shahriar & Weinreich, Ilona & Reinarz, Dominik, 2005. "Wavelet-based prediction of oil prices," Chaos, Solitons & Fractals, Elsevier, vol. 25(2), pages 265-275.
    13. Bhandari, Avishek, 2020. "Long memory and fractality among global equity markets: A multivariate wavelet approach," MPRA Paper 99653, University Library of Munich, Germany.
    14. Duchesne, Pierre, 2006. "Testing for multivariate autoregressive conditional heteroskedasticity using wavelets," Computational Statistics & Data Analysis, Elsevier, vol. 51(4), pages 2142-2163, December.
    15. Liu, Xiaoquan & Cao, Yi & Ma, Chenghu & Shen, Liya, 2019. "Wavelet-based option pricing: An empirical study," European Journal of Operational Research, Elsevier, vol. 272(3), pages 1132-1142.
    16. Heni Boubaker, 2020. "Wavelet Estimation Performance of Fractional Integrated Processes with Heavy-Tails," Computational Economics, Springer;Society for Computational Economics, vol. 55(2), pages 473-498, February.
    17. Ysusi Carla, 2009. "Analysis of the Dynamics of Mexican Inflation Using Wavelets," Working Papers 2009-09, Banco de México.
    18. 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.
    19. Zhou, Yong & Wan, Alan T.K. & Xie, Shangyu & Wang, Xiaojing, 2010. "Wavelet analysis of change-points in a non-parametric regression with heteroscedastic variance," Journal of Econometrics, Elsevier, vol. 159(1), pages 183-201, November.
    20. Benjamin M. Tabak, 2007. "Estimating the Fractional Order of Integration of Yields in the Brazilian Fixed Income Market," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 36(3), pages 231-246, November.

    More about this item

    Keywords

    ;

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

    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:bca:bocawp:02-3. 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/bocgvca.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.