Thanks Paul, for both your finds
W
-----Original Message-----
From: SPSSX(r) Discussion [mailto:
[hidden email]] On Behalf Of
Paul Ginns
Sent: Monday, March 12, 2007 9:08 PM
To:
[hidden email]
Subject: Using heteroskedasticity-consistent standard error estimators in
OLS regression
Hi all,
Here's another newly available SPSS macro (from Andrew Hayes
http://www.comm.ohio-state.edu/ahayes/) for those who regress regularly:
Hayes, A. F. & Cai, L. (in press). Using heteroskedasticity-consistent
standard error estimators in OLS regression: An introduction and software
implementation. [PDF <
http://www.comm.ohio-state.edu/ahayes/hcse.pdf> ].
Behavior Research Methods. Here are the macros in this paper in electronic
<
http://www.comm.ohio-state.edu/ahayes/SPSS%20programs/HCSEp.htm> format.
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 hokoscedasticity 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.
Cheers,
Paul
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