Representativeness and Efficiency in Overidentified IV

Chun Pang Chow and Hiroyuki Kasahara

Papers from arXiv.org

Abstract: Under heterogeneous treatment effects, the GMM weighting matrix in overidentified IV models dictates the estimand. We show that efficient GMM downeights high-variance instruments and frequently assigning negative weights that undermine causal interpretation. Moreover, GMM cannot simultaneously achieve efficiency and accommodate researcher-specified weights. We resolve this trade-off by developing the Representative Targeting (RT) estimator. By averaging instrument-specific Wald estimators under Positive Regression Dependence, RT ensures non-negative weights while achieving the semiparametric efficiency bound for its targeted estimand. We demonstrate the heterogeneity penalty empirically in a class-size experiment and apply RT to recover the Policy-Relevant Treatment Effect within a patent leniency design.

Date: 2026-04
New Economics Papers: this item is included in nep-ecm and nep-exp
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