An Improved Inference for IV Regressions

Fuente: arXiv
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Auteurs principaux: Dou, Liyu, Min, Pengjin, Wang, Wenjie, Zhang, Yichong
Format: Preprint
Publié: 2025
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author Dou, Liyu
Min, Pengjin
Wang, Wenjie
Zhang, Yichong
author_facet Dou, Liyu
Min, Pengjin
Wang, Wenjie
Zhang, Yichong
contents Empirical instrumental variables (IV) studies often report separate results based on low-dimensional instruments and many base instruments. This paper proposes a combination test that integrates these commonly reported statistics. The test linearly combines a cluster-robust Wald statistic based on low-dimensional IVs with leave-one-cluster-out Lagrangian Multiplier (LM) and Anderson-Rubin (AR) statistics constructed from many IVs. We establish joint asymptotic normality and asymptotic optimality of the proposed test. The procedure yields costless efficiency improvements, automatically adapts to weak identification of many instruments, and is accompanied by a practical rule of thumb for assessing efficiency gains.
format Preprint
id arxiv_https___arxiv_org_abs_2506_23816
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle An Improved Inference for IV Regressions
Dou, Liyu
Min, Pengjin
Wang, Wenjie
Zhang, Yichong
Econometrics
Empirical instrumental variables (IV) studies often report separate results based on low-dimensional instruments and many base instruments. This paper proposes a combination test that integrates these commonly reported statistics. The test linearly combines a cluster-robust Wald statistic based on low-dimensional IVs with leave-one-cluster-out Lagrangian Multiplier (LM) and Anderson-Rubin (AR) statistics constructed from many IVs. We establish joint asymptotic normality and asymptotic optimality of the proposed test. The procedure yields costless efficiency improvements, automatically adapts to weak identification of many instruments, and is accompanied by a practical rule of thumb for assessing efficiency gains.
title An Improved Inference for IV Regressions
topic Econometrics
url https://arxiv.org/abs/2506.23816