Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/282635 
Year of Publication: 
2023
Series/Report no.: 
IZA Discussion Papers No. 16508
Publisher: 
Institute of Labor Economics (IZA), Bonn
Abstract: 
In linear regression models, measurement error in a covariate causes Ordinary Least Squares (OLS) to be biased and inconsistent. Instrumental Variables (IV) is a common solution. While IV is also biased, it is consistent. Here, we undertake an asymptotic comparison of OLS and IV in the case where a covariate is mismeasured for [Nδ] of N observations with δ ∊ [0, 1]. We show that OLS is consistent for δ < 1 and is asymptotically normal and more efficient than IV for δ < 0.5. Simulations and an application to the impact of body mass index on family income demonstrate the practical usefulness of this result.
Subjects: 
errors-in-variables
measurement error
asymptotics
JEL: 
C13
C26
C52
Document Type: 
Working Paper

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