IDEAS home Printed from
   My bibliography  Save this paper

A general framework for testing the Granger noncausality hypothesis


  • Péguin-Feissolle, Anne

    () (GREQAM-CNRS)

  • Teräsvirta, Timo

    () (Dept. of Economic Statistics, Stockholm School of Economics)


In this paper, new noncausality tests relying on a general nonlinear framework are proposed and their performance studied by a Monte Carlo experiment and a variety of nonlinear artificial series. Two of the tests are based on a Taylor expansion of the nonlinear model around a given point in the sample space. Yet another test is based on artificial neural networks. The tests appear to be well-sized and have good power properties.

Suggested Citation

  • Péguin-Feissolle, Anne & Teräsvirta, Timo, 1999. "A general framework for testing the Granger noncausality hypothesis," SSE/EFI Working Paper Series in Economics and Finance 343, Stockholm School of Economics.
  • Handle: RePEc:hhs:hastef:0343

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Other versions of this item:

    References listed on IDEAS

    1. R. Bhar & C. Chiarella, 1997. "Transformation of Heath?Jarrow?Morton models to Markovian systems," The European Journal of Finance, Taylor & Francis Journals, vol. 3(1), pages 1-26.
    2. Björk, Tomas & Gombani, Andrea, 1997. "Minimal Realizations of Forward Rates," SSE/EFI Working Paper Series in Economics and Finance 182, Stockholm School of Economics.
    3. Carl Chiarella & Oh Kang Kwon, 2001. "Forward rate dependent Markovian transformations of the Heath-Jarrow-Morton term structure model," Finance and Stochastics, Springer, vol. 5(2), pages 237-257.
    4. Inui, Koji & Kijima, Masaaki, 1998. "A Markovian Framework in Multi-Factor Heath-Jarrow-Morton Models," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 33(03), pages 423-440, September.
    5. Robert A. Jarrow, 2009. "The Term Structure of Interest Rates," Annual Review of Financial Economics, Annual Reviews, vol. 1(1), pages 69-96, November.
    6. Björk, Tomas & Landen, Camilla, 2000. "On the construction of finite dimensional realizations for nonlinear forward rate models," SSE/EFI Working Paper Series in Economics and Finance 420, Stockholm School of Economics.
    7. Vasicek, Oldrich, 1977. "An equilibrium characterization of the term structure," Journal of Financial Economics, Elsevier, vol. 5(2), pages 177-188, November.
    8. Vasicek, Oldrich Alfonso, 1977. "Abstract: An Equilibrium Characterization of the Term Structure," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 12(04), pages 627-627, November.
    9. Hull, John & White, Alan, 1990. "Pricing Interest-Rate-Derivative Securities," Review of Financial Studies, Society for Financial Studies, vol. 3(4), pages 573-592.
    10. Nelson, Charles R & Siegel, Andrew F, 1987. "Parsimonious Modeling of Yield Curves," The Journal of Business, University of Chicago Press, vol. 60(4), pages 473-489, October.
    Full references (including those not matched with items on IDEAS)


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

    Cited by:

    1. Ruist, Erik, 2007. "The choice between two hypothesis tests," SSE/EFI Working Paper Series in Economics and Finance 667, Stockholm School of Economics.
    2. Prasad Bal, Debi & Narayan Rath, Badri, 2015. "Nonlinear causality between crude oil price and exchange rate: A comparative study of China and India," Energy Economics, Elsevier, vol. 51(C), pages 149-156.
    3. Nevin Yörük & Cumhur Erdem & Meziyet Sema Erdem, 2006. "Testing for linear and nonlinear Granger Causality in the stock price--volume relation: Turkish banking firms’ evidence," Applied Financial Economics Letters, Taylor and Francis Journals, vol. 2(3), pages 165-171, May.
    4. Philip Rothman & Dick van Dijk & Philip Hans Franses, 1999. "A Multivariate STAR Analysis of the Relationship Between Money and Output," Working Papers 9913, East Carolina University, Department of Economics.
    5. Ahmad Zubaidi Baharumshah & Venus Khim-Sen Liew & Evan Lau, 2003. "Nonlinear Mean Reversion in Real Exchange Rates: Evidence from the ASEAN-5," International Trade 0308001, EconWPA.
    6. Tiwari, Aviral Kumar & Dar, Arif Billah & Bhanja, Niyati, 2013. "Oil price and exchange rates: A wavelet based analysis for India," Economic Modelling, Elsevier, vol. 31(C), pages 414-422.
    7. Werner Kristjanpoller R. & Alejandro Sierra C., 2014. "Relationship between the dollar, the price of copper and the IPSA indifferent time scales: An approach through Wavelet," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 17(3), pages 56-85, December.
    8. Chen, Show-Lin & Tsai, Li-Ju & Wu, Jyh-Lin, 2004. "A revisit to liquidity effects--evidence from a non-linear approach," Journal of Macroeconomics, Elsevier, vol. 26(3), pages 501-517, September.
    9. João Paulo Martin Faleiros & Denisard Cnéio de Oliveira Alves, 2008. "Modelo de Crescimento Baseado nas Exportações: Evidências empíricas para Chile, Brasil e México, em uma perspectiva Não Linear," Anais do XXXVI Encontro Nacional de Economia [Proceedings of the 36th Brazilian Economics Meeting] 200807170923500, ANPEC - Associação Nacional dos Centros de Pósgraduação em Economia [Brazilian Association of Graduate Programs in Economics].

    More about this item


    Hypothesis testing; causality; artificial neural networks; nonlinearity;

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

    NEP fields

    This paper has been announced in the following NEP Reports:


    Access and download statistics


    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:hhs:hastef:0343. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Helena Lundin). General contact details of provider: .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.