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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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
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Bias
Intelligence
Regression analysis
Statistics
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creator Marais, M Laurentius
Wecker, William E
Waternaux, Christine
Petkova, Eva
description 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.
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source International Bibliography of the Social Sciences (IBSS); JSTOR Archival Journals and Primary Sources Collection; ABI/INFORM Global; Taylor and Francis Science and Technology Collection
subjects Bias
Intelligence
Regression analysis
Statistics
title 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
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