IDEAS home Printed from https://ideas.repec.org/a/cup/macdyn/v3y1999i01p69-83_01.html
   My bibliography  Save this article

Detection And Modeling Of Regression Parameter Variation Across Frequencies

Author

Listed:
  • Tan, Hui Boon
  • Ashley, Richard

Abstract

A simple technique for directly testing the parameters of a time-series regression model for instability across frequencies is presented. The method can be implemented easily in the time domain, so that parameter instability across frequency bands can be conveniently detected and modeled in conjunction with other econometric features of the problem at hand, such as simultaneity, cointegration, missing observations, and cross-equation restrictions. The usefulness of the new technique is illustrated with an application to a cointegrated consumption-income regression model, yielding a straightforward test of the permanent income hypothesis.

Suggested Citation

  • Tan, Hui Boon & Ashley, Richard, 1999. "Detection And Modeling Of Regression Parameter Variation Across Frequencies," Macroeconomic Dynamics, Cambridge University Press, vol. 3(1), pages 69-83, March.
    • Handle: RePEc:cup:macdyn:v:3:y:1999:i:01:p:69-83_01
    as

    Download full text from publisher

    File URL: https://www.cambridge.org/core/product/identifier/S1365100599010032/type/journal_article
    File Function: link to article abstract page
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Rui Pascoal, 2012. "Macroeconomic Factors of Household Default. Is There Myopic Behaviour?," GEMF Working Papers 2012-20, GEMF, Faculty of Economics, University of Coimbra.
    2. Ciner, Cetin, 2013. "Oil and stock returns: Frequency domain evidence," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 23(C), pages 1-11.
    3. Emmanuel Anoruo & Vasudeva N. R. Murthy, 2017. "An examination of the REIT return–implied volatility relation: a frequency domain approach," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 41(3), pages 581-594, July.
    4. Ralf Becker & Walter Enders & Stan Hurn, 2001. "Modelling Structural Change in Money Demand Using a Fourier-Series Approximation," Research Paper Series 67, Quantitative Finance Research Centre, University of Technology, Sydney.
    5. Richard A. Ashley. & Randall J. Verbrugge., 2006. "Mis-Specification and Frequency Dependence in a New Keynesian Phillips Curve," Working Papers e06-12, Virginia Polytechnic Institute and State University, Department of Economics.
    6. Richard Ashley & Randal Verbrugge, 2009. "Frequency Dependence in Regression Model Coefficients: An Alternative Approach for Modeling Nonlinear Dynamic Relationships in Time Series," Econometric Reviews, Taylor & Francis Journals, vol. 28(1-3), pages 4-20.
    7. Feng Zhu, 2005. "The fragility of the Phillips curve: A bumpy ride in the frequency domain," BIS Working Papers 183, Bank for International Settlements.
    8. Wei Yanfeng, 2013. "The Dynamic Relationships between Oil Prices and the Japanese Economy: A Frequency Domain Analysis," Review of Economics & Finance, Better Advances Press, Canada, vol. 3, pages 57-67, May.
    9. Chan, Wing Hong & Le, Minh & Wu, Yan Wendy, 2019. "Holding Bitcoin longer: The dynamic hedging abilities of Bitcoin," The Quarterly Review of Economics and Finance, Elsevier, vol. 71(C), pages 107-113.
    10. Richard Ashley & Randal J. Verbrugge, 2015. "Persistence Dependence in Empirical Relations: The Velocity of Money," Working Papers (Old Series) 1530, Federal Reserve Bank of Cleveland.
    11. Fabio Busetti & Michele Caivano, 2017. "Low frequency drivers of the real interest rate: a band spectrum regression approach," Temi di discussione (Economic working papers) 1132, Bank of Italy, Economic Research and International Relations Area.
    12. Ralf Becker & Walter Enders & A. Stan Hurn, 2001. "Testing for Time Dependence in Parameters," Research Paper Series 58, Quantitative Finance Research Centre, University of Technology, Sydney.
    13. Richard A. Ashley & Kwok Ping Tsang, 2014. "Credible Granger-Causality Inference with Modest Sample Lengths: A Cross-Sample Validation Approach," Econometrics, MDPI, Open Access Journal, vol. 2(1), pages 1-20, March.
    14. Richard A. Ashley & Randall J. Verbrugge., 2006. "Mis-Specification in Phillips Curve Regressions: Quantifying Frequency Dependence in This Relationship While Allowing for Feedback," Working Papers e06-11, Virginia Polytechnic Institute and State University, Department of Economics.

    More about this item

    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:cup:macdyn:v:3:y:1999:i:01:p:69-83_01. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Kirk Stebbing (email available below). General contact details of provider: https://www.cambridge.org/mdy .

    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.