IDEAS home Printed from https://ideas.repec.org/p/zbw/fziddp/602012.html
   My bibliography  Save this paper

SPECTRAN, a set of Matlab programs for Spectral analysis

Author

Listed:
  • Marczak, Martyna
  • Gómez, Víctor

Abstract

Spectral analysis is one of the most important areas of time series econometrics. The use of spectral measures is widespread in different science fields such as economics, physics, engineering, geology. The SPECTRAN toolbox has been developed to facilitate the application of spectral concepts to univariate as well as to multivariate series. It offers a variety of frequency-domain techniques and supports the statistical inference. It also provides convenient tools for the examination of the results, e.g.functions for writing the output to a file or functions specially designed for plotting the estimated spectral measures. The key feature of SPECTRAN is the user-friendliness embodied in, e.g., the central function spectran which performs the whole analysis with default settings, but also gives the user the possibility to adjust them. This document sets out the most relevant spectral concepts and their implementation in SPECTRAN. Finally, three examples shall illustrate the application of different toolbox function to macroeconomic data.

Suggested Citation

  • Marczak, Martyna & Gómez, Víctor, 2012. "SPECTRAN, a set of Matlab programs for Spectral analysis," FZID Discussion Papers 60-2012, University of Hohenheim, Center for Research on Innovation and Services (FZID).
    • Handle: RePEc:zbw:fziddp:602012
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/67382/1/73053281X.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Martyna Marczak & Thomas Beissinger, 2013. "Real wages and the business cycle in Germany," Empirical Economics, Springer, vol. 44(2), pages 469-490, April.
    2. Berens, Philipp, 2009. "CircStat: A MATLAB Toolbox for Circular Statistics," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 31(i10).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Paul Beaudry & Dana Galizia & Franck Portier, 2020. "Putting the Cycle Back into Business Cycle Analysis," American Economic Review, American Economic Association, vol. 110(1), pages 1-47, January.
    2. Martyna Marczak & Víctor Gómez, 2017. "Monthly US business cycle indicators: a new multivariate approach based on a band-pass filter," Empirical Economics, Springer, vol. 52(4), pages 1379-1408, June.

    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. Marczak, Martyna & Gómez, Víctor, 2015. "Cyclicality of real wages in the USA and Germany: New insights from wavelet analysis," Economic Modelling, Elsevier, vol. 47(C), pages 40-52.
    2. Eva Lajtkepová, 2020. "Distribution of Wages in the Regions of the Czech Republic," ACTA VSFS, University of Finance and Administration, vol. 14(2), pages 123-136.
    3. Jennifer B Tennessen & Marla M Holt & Brianna M Wright & M Bradley Hanson & Candice K Emmons & Deborah A Giles & Jeffrey T Hogan & Sheila J Thornton & Volker B Deecke, 2023. "Divergent foraging strategies between populations of sympatric matrilineal killer whales," Behavioral Ecology, International Society for Behavioral Ecology, vol. 34(3), pages 373-386.
    4. Thomas Schreiner & Marit Petzka & Tobias Staudigl & Bernhard P. Staresina, 2023. "Respiration modulates sleep oscillations and memory reactivation in humans," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    5. Martyna Marczak & Víctor Gómez, 2017. "Monthly US business cycle indicators: a new multivariate approach based on a band-pass filter," Empirical Economics, Springer, vol. 52(4), pages 1379-1408, June.
    6. Thomas Schreiner & Elisabeth Kaufmann & Soheyl Noachtar & Jan-Hinnerk Mehrkens & Tobias Staudigl, 2022. "The human thalamus orchestrates neocortical oscillations during NREM sleep," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    7. Martyna Marczak & Thomas Beissinger, 2016. "Bidirectional relationship between investor sentiment and excess returns: new evidence from the wavelet perspective," Applied Economics Letters, Taylor & Francis Journals, vol. 23(18), pages 1305-1311, December.
    8. Arthur Pewsey & Eduardo García-Portugués, 2021. "Recent advances in directional statistics," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(1), pages 1-58, March.
    9. César Henrique Mattos Pires & Felipe M. Pimenta & Carla A. D'Aquino & Osvaldo R. Saavedra & Xuerui Mao & Arcilan T. Assireu, 2020. "Coastal Wind Power in Southern Santa Catarina, Brazil," Energies, MDPI, vol. 13(19), pages 1-23, October.
    10. Alexis T Baria & Brian Maniscalco & Biyu J He, 2017. "Initial-state-dependent, robust, transient neural dynamics encode conscious visual perception," PLOS Computational Biology, Public Library of Science, vol. 13(11), pages 1-29, November.
    11. Matthijs J. Warrens & Bunga C. Pratiwi, 2016. "Kappa Coefficients for Circular Classifications," Journal of Classification, Springer;The Classification Society, vol. 33(3), pages 507-522, October.
    12. Miyamoto, Hiroaki, 2015. "Cyclical behavior of real wages in Japan," Economics Letters, Elsevier, vol. 130(C), pages 56-59.
    13. Lombard, F. & Hawkins, Douglas M. & Potgieter, Cornelis J., 2017. "Sequential rank CUSUM charts for angular data," Computational Statistics & Data Analysis, Elsevier, vol. 105(C), pages 268-279.
    14. Masataka Sawayama & Shin'ya Nishida, 2018. "Material and shape perception based on two types of intensity gradient information," PLOS Computational Biology, Public Library of Science, vol. 14(4), pages 1-40, April.
    15. Aguiar-Conraria, Luis & Martins, Manuel M.F. & Soares, Maria Joana, 2018. "Estimating the Taylor rule in the time-frequency domain," Journal of Macroeconomics, Elsevier, vol. 57(C), pages 122-137.
    16. Assaf Breska & Leon Y Deouell, 2017. "Neural mechanisms of rhythm-based temporal prediction: Delta phase-locking reflects temporal predictability but not rhythmic entrainment," PLOS Biology, Public Library of Science, vol. 15(2), pages 1-30, February.
    17. Sunny Nigam & Russell Milton & Sorin Pojoga & Valentin Dragoi, 2023. "Adaptive coding across visual features during free-viewing and fixation conditions," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    18. Daniel S. Kluger & Carina Forster & Omid Abbasi & Nikos Chalas & Arno Villringer & Joachim Gross, 2023. "Modulatory dynamics of periodic and aperiodic activity in respiration-brain coupling," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    19. Dima, Bogdan & Dima, Ştefana Maria, 2017. "Energy consumption synchronization between Europe, United States and Japan: A spectral analysis assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 1261-1271.
    20. Manuela Costa & Diego Lozano-Soldevilla & Antonio Gil-Nagel & Rafael Toledano & Carina R. Oehrn & Lukas Kunz & Mar Yebra & Costantino Mendez-Bertolo & Lennart Stieglitz & Johannes Sarnthein & Nikolai , 2022. "Aversive memory formation in humans involves an amygdala-hippocampus phase code," Nature Communications, Nature, vol. 13(1), pages 1-16, December.

    More about this item

    Keywords

    univariate spectral analysis; multivariate spectral analysis; Matlab;
    All these keywords.

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • 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
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software

    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:zbw:fziddp:602012. 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: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/fihohde.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.