Post-Instrument Bias in Linear Models

Adam Glynn, Miguel Rueda and Julian Schuessler
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Julian Schuessler: Aarhus University

No axn4t, SocArXiv from Center for Open Science

Abstract: Post-instrument covariates are often included as controls in IV analyses to address a violation of the exclusion restriction. However, we show that such analyses are subject to biases unless strong assumptions hold. Using linear constant-effects models, we present asymptotic bias formulas for three estimators (with and without measurement error): IV with post-instrument covariates, IV without post-instrument covariates, and OLS. In large samples and when the model provides a reasonable approximation, these formulas sometimes allow the analyst to bracket the parameter of interest with two estimators and allow the analyst to choose the estimator with the least asymptotic bias. We illustrate these points with a discussion of Acemoglu, Johnson, and Robinson (2001).

Date: 2023-01-13
New Economics Papers: this item is included in nep-ecm
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Persistent link: https://EconPapers.repec.org/RePEc:osf:socarx:axn4t

DOI: 10.31219/osf.io/axn4t

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