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 the 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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- 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.
- Li, Jing, 2006. "Testing Granger Causality in the presence of threshold effects," International Journal of Forecasting, Elsevier, vol. 22(4), pages 771-780.
- 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.
- 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.
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