| Name: | Description: | Size: | Format: | |
|---|---|---|---|---|
| 481.8 KB | Adobe PDF |
Advisor(s)
Abstract(s)
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.
Description
Keywords
Heteroskedasticity testing White test Wald test Supremum
Pedagogical Context
Citation
Murteira, José M.R.; Esmeralda A. Ramalho and Joaquim J.S. Ramalho (2013). "Heteroskedasticity testing through a comparison of Wald statistics". Portuguese Economic Journal, Vol. 12, No. 2: pp. 131-160
Publisher
Springer Verlag