A bias test for heteroscedastic linear least squares regression

Eric Blankmeyer

MPRA Paper from University Library of Munich, Germany

Abstract: A correlation between regressors and disturbances presents challenging problems in linear regression. Issues like omitted variables, measurement error and simultaneity render ordinary least squares (OLS) biased and inconsistent. In the context of heteroscedastic linear regression, this note proposes a bias test that is simple to apply. It does not reveal the size or sign of OLS bias but instead provides a statistic to assess the probable presence or absence of bias. The test is examined in simulation and in real data sets.

Keywords: Linear regression; least squares bias; heteroscedasticity; Fisher transformation (search for similar items in EconPapers)
JEL-codes: C1 C13 C2 C3 (search for similar items in EconPapers)
Date: 2022
New Economics Papers: this item is included in nep-ecm
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:116605

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