Testing the Granger noncausality hypothesis in stationary nonlinear models of unknown functional form
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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|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.|
|Note:||This is a revised version (January 2012) of the paper.|
|Contact details of provider:|| Postal: The Economic Research Institute, Stockholm School of Economics, P.O. Box 6501, 113 83 Stockholm, Sweden|
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- Bénédicte Vidaillet & V. D'Estaintot & P. Abécassis, 2005. "Introduction," Post-Print hal-00287137, HAL.
- Li, Jing, 2006. "Testing Granger Causality in the presence of threshold effects," International Journal of Forecasting, Elsevier, vol. 22(4), pages 771-780.
- Ashley, R & Granger, C W J & Schmalensee, R, 1980. "Advertising and Aggregate Consumption: An Analysis of Causality," Econometrica, Econometric Society, vol. 48(5), pages 1149-1167, July.
- Davidson, Russell & MacKinnon, James G, 1998.
"Graphical Methods for Investigating the Size and Power of Hypothesis Tests,"
The Manchester School of Economic & Social Studies,
University of Manchester, vol. 66(1), pages 1-26, January.
- 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.
- 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.
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