Tests for independence in non-parametric heteroscedastic regression models
Consistent procedures are constructed for testing independence between the regressor and the error in non-parametric regression models. The tests are based on the Fourier formulation of independence, and utilize the joint and the marginal empirical characteristic functions of the regressor and of estimated residuals. The asymptotic null distribution as well as the behavior of the test statistic under alternatives is investigated. A simulation study compares bootstrap versions of the proposed tests to corresponding procedures utilizing the empirical distribution function.
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Volume (Year): 102 (2011)
Issue (Month): 4 (April)
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- Einmahl, J.H.J. & van Keilegom, I., 2008.
"Tests for independence in nonparametric regression,"
Other publications TiSEM
4356c520-d1d5-4156-b5b7-0, Tilburg University, School of Economics and Management.
- Einmahl, J.H.J. & van Keilegom, I., 2006. "Tests for Independence in Nonparametric Regression," Discussion Paper 2006-80, Tilburg University, Center for Economic Research.
- Meintanis, Simos G. & Iliopoulos, George, 2008. "Fourier methods for testing multivariate independence," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 1884-1895, January.
- Neumeyer, Natalie, 2009. "Testing independence in nonparametric regression," Journal of Multivariate Analysis, Elsevier, vol. 100(7), pages 1551-1566, August.
- Bilodeau, M. & Lafaye de Micheaux, P., 2005. "A multivariate empirical characteristic function test of independence with normal marginals," Journal of Multivariate Analysis, Elsevier, vol. 95(2), pages 345-369, August.
- Einmahl, John H.J. & Van Keilegom, Ingrid, 2008.
"Specification tests in nonparametric regression,"
Journal of Econometrics,
Elsevier, vol. 143(1), pages 88-102, March.
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