Export Publication

Murteira, J. M. R., Ramalho, E. A. & Ramalho, J. J. S. (2013). Heteroskedasticity testing through a comparison of Wald statistics. Portuguese Economic Journal. 12 (2), 131-160

J. Murteira et al.,  "Heteroskedasticity testing through a comparison of Wald statistics", in Portuguese Economic Journal, vol. 12, no. 2, pp. 131-160, 2013

@article{murteira2013_1765610920182,
	author = "Murteira, J. M. R. and Ramalho, E. A. and Ramalho, J. J. S.",
	title = "Heteroskedasticity testing through a comparison of Wald statistics",
	journal = "Portuguese Economic Journal",
	year = "2013",
	volume = "12",
	number = "2",
	doi = "10.1007/s10258-013-0087-x",
	pages = "131-160",
	url = "http://link.springer.com/article/10.1007%2Fs10258-013-0087-x"
}

TY  - JOUR
TI  - Heteroskedasticity testing through a comparison of Wald statistics
T2  - Portuguese Economic Journal
VL  - 12
IS  - 2
AU  - Murteira, J. M. R.
AU  - Ramalho, E. A.
AU  - Ramalho, J. J. S.
PY  - 2013
SP  - 131-160
SN  - 1617-982X
DO  - 10.1007/s10258-013-0087-x
UR  - http://link.springer.com/article/10.1007%2Fs10258-013-0087-x
AB  - This paper shows that a test for heteroskedasticity within the context of classical linear regression can be based on the difference between Wald statistics in heteroskedasticity-robust and nonrobust forms. The test is asymptotically distributed under the null hypothesis of homoskedasticity as chi-squared with one degree of freedom. The power of the test is sensitive to the choice of parametric restriction used by the Wald statistics, so the supremum of a range of individual test statistics is proposed. Two versions of a supremum-based test are considered: the first version does not have a known asymptotic null distribution, so the bootstrap is employed to approximate its empirical distribution. The second version has a known asymptotic distribution and, in some cases, is asymptotically pivotal under the null. A simulation study illustrates the use and finite-sample performance of both versions of the test. In this study, the bootstrap is found to provide better size control than asymptotic critical values, namely with heavy-tailed, asymmetric distributions of the covariates. In addition, the use of well-known modifications of the heteroskedasticity consistent covariance matrix estimator of OLS coefficients is also found to benefit the tests’ overall behaviour.
ER  -