Correcting for omitted-variables and measurement-error bias in regression with an application to the effect of lead on IQ / Comment: Problems with using auxiliary information to correct for omitted variables when estimating the effect of lead on IQ / Comment / Rejoinder

Ordinary least squares (OLS) regression estimates are biased, in general, when relevant variables are omitted from the regression equation or when included variables are measured with error. The errors-in-variables bias can be corrected using auxiliary information about unobservable measurement erro...

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Bibliographic Details
Published in:Journal of the American Statistical Association 1998-06, Vol.93 (442), p.494
Main Authors: Marais, M Laurentius, Wecker, William E, Waternaux, Christine, Petkova, Eva
Format: Article
Language:English
Subjects:
Bias
Intelligence
Regression analysis
Statistics
Online Access:Get full text
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Summary:Ordinary least squares (OLS) regression estimates are biased, in general, when relevant variables are omitted from the regression equation or when included variables are measured with error. The errors-in-variables bias can be corrected using auxiliary information about unobservable measurement errors. In this article we demonstrate how auxiliary information can also be used to correct for omitted-variables bias. We illustrate our methods with an application to four published studies of the effect on IQ of childhood exposure to lead.
ISSN:0162-1459
1537-274X