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Testing the Granger noncausality hypothesis in stationary nonlinear models of unknown functional form

  • Péguin-Feissolle, Anne



  • Strikholm, Birgit


    (Bank of Estonia)

  • Teräsvirta, Timo


    (CREATES, Department of Economics and Business)

In this paper we propose a general method for testing the Granger noncausality hypothesis in stationary nonlinear models of unknown functional form. These tests are based on a Taylor expansion of the nonlinear model around a given point in a sample space. We study the performance of our tests by a Monte Carlo experiment and compare these to the most widely used linear test. Our tests appear to be well-sized and have reasonably good power properties.

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Paper provided by Stockholm School of Economics in its series SSE/EFI Working Paper Series in Economics and Finance with number 672.

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Length: 30 pages
Date of creation: 27 Aug 2007
Date of revision: 18 Jan 2012
Publication status: Published as Péguin-Feissolle, Anne, Birgit Strikholm and Timo Teräsvirta, 'Testing the Granger noncausality hypothesis in stationary nonlinear models of unknown functional form' in Communications in Statistics - Simulation and Computation, 2013, pages 1063-1087.
Handle: RePEc:hhs:hastef:0672
Note: This is a revised version (January 2012) of the paper.
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  1. Bell, David & Kay, Jim & Malley, Jim, 1996. "A non-parametric approach to non-linear causality testing," Economics Letters, Elsevier, vol. 51(1), pages 7-18, April.
  2. Bénédicte Vidaillet & V. D'Estaintot & P. Abécassis, 2005. "Introduction," Post-Print hal-00287137, HAL.
  3. Ashley, R & Granger, C W J & Schmalensee, R, 1980. "Advertising and Aggregate Consumption: An Analysis of Causality," Econometrica, Econometric Society, vol. 48(5), pages 1149-67, July.
  4. Russell Davidson & James G. MacKinnon, 1994. "Graphical Methods for Investigating the Size and Power of Hypothesis Tests," Working Papers 903, Queen's University, Department of Economics.
  5. Li, Jing, 2006. "Testing Granger Causality in the presence of threshold effects," International Journal of Forecasting, Elsevier, vol. 22(4), pages 771-780.
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