Abstract
This paper presents a parameter covariance matrix estimator which is consistent even when the disturbances of a linear regression model are heteroskedastic. This estimator does not depend on a formal model of the structure of the heteroskedasticity. By comparing the elements of the new estimator to those of the usual covariance estimator, one obtains a direct test for heteroskedasticity, since in the absence of heteroskedasticity, the two estimators will be approximately equal, but will generally diverge otherwise. The test has an appealing least squares interpretation.
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Publication Info
- Year
- 1980
- Type
- article
- Volume
- 48
- Issue
- 4
- Pages
- 817-817
- Citations
- 25667
- Access
- Closed
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Identifiers
- DOI
- 10.2307/1912934