Using heteroskedasticity-consistent standard error estimators in OLS regression: An introduction and software implementation
Using heteroskedasticity-consistent standard error estimators in OLS regression: An introduction and software implementation
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DOI：
10.3758/bf03192961
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发表时间：
2007-11-01
影响因子：
5.4
通讯作者：
Cai, Li
中科院分区：
文献类型：
--
作者：
Hayes, Andrew F.;Cai, Li
同方差性是普通最小二乘（OLS）回归中的一个重要假设。虽然当违背同方差假设时，OLS回归中回归参数的估计是无偏的，但在异方差假设下，参数估计的协方差矩阵的估计可能是有偏的和不一致的，这可能产生显著性检验和置信区间，可以是自由的或保守的。在简要描述了异方差性及其对OLS回归推断的影响后，我们讨论了OLS回归的异方差性一致的标准误估计量，并认为研究人员在使用OLS回归进行假设检验时应经常使用这些估计量之一。为了便于采纳这一建议，我们提供了易于使用的SPSS和SAS宏来实现这里讨论的过程。
Homoskedasticity is an important assumption in ordinary least squares (OLS) regression. Although the estimator of the regression parameters in OLS regression is unbiased when the homoskedasticity assumption is violated, the estimator of the covariance matrix of the parameter estimates can be biased and inconsistent under heteroskedasticity, which can produce significance tests and confidence intervals that can be liberal or conservative. After a brief description of heteroskedasticity and its effects on inference in OLS regression, we discuss a family of heteroskedasticity-consistent standard error estimators for OLS regression and argue investigators should routinely use one of these estimators when conducting hypothesis tests using OLS regression. To facilitate the adoption of this recommendation, we provide easy-to-use SPSS and SAS macros to implement the procedures discussed here.