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HIGHER-ORDER APPROXIMATION OF IV ESTIMATORS WITH INVALID INSTRUMENTS

Published online by Cambridge University Press:  10 November 2022

Byunghoon Kang Open the ORCID record for Byunghoon Kang [Opens in a new window]
Byunghoon Kang*
Affiliation:
Lancaster University
*
Address correspondence to Byunghoon Kang, Department of Economics, Lancaster University, Lancaster, UK; e-mail: [email protected].
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Abstract

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This paper analyzes the higher-order approximation of instrumental variable (IV) estimators in a linear homoskedastic IV regression model when a large set of instruments with potential invalidity is present. We establish theoretical results on the higher-order mean-squared error (MSE) approximation of the two-stage least-squares (2SLS), the limited information maximum likelihood (LIML), the Fuller (FULL), the bias-adjusted 2SLS, and jackknife version of the LIML and FULL estimators by allowing for local violations of the instrument exogeneity conditions. Based on the approximation to the higher-order MSE, we consider the instrument selection criteria that can be used to choose among the set of available instruments. We demonstrate the asymptotic optimality of the instrument selection procedure proposed by Donald and Newey (2001, Econometrica 69, 1161–1191) in the presence of locally (faster than $N^{-1/2}$) invalid instruments in the sense that the dominant term in the MSE with the chosen instrument is asymptotically equivalent to the infeasible optimum. Furthermore, we propose instrument selection procedures to choose instruments among the sets of conservative (known) valid instruments and potentially locally ($N^{-1/2}$) invalid instruments based on the higher-order MSE of the IV estimators by considering the bias-variance trade-off.

Type
ARTICLES
Information
Econometric Theory , Volume 40 , Issue 4 , August 2024 , pp. 752 - 789
Copyright
© The Author(s), 2022. Published by Cambridge University Press

Footnotes

We thank the Editor (Peter Phillips), the Co-Editor (Patrik Guggenberger), and three anonymous referees for thoughtful comments that significantly improved the paper. The author is grateful to Bruce Hansen for his thoughtful discussions. The author also acknowledges helpful conversations with Jack Porter, Xiaoxia Shi, Francis DiTraglia, Joachim Freyberger, Seojeong Lee, Adam McCloskey, and seminar participants at UW–Madison, MEG 2013, and NEM 2019. This paper is a revised version of the second chapter in the author’s Ph.D. thesis at UW–Madison, and the previous version was also circulated under the title “Choosing a Set of Instruments among Many and Possibly Invalid Instruments.”

References

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