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Abstract
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.