When Is GMM Actually LATE? Weighting Matrices and Causal Interpretation in Overidentified IV

Chun Pang Chow and Hiroyuki Kasahara

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Abstract: Under heterogeneous treatment effects, the weighting matrix of overidentified IV-GMM selects the estimand, not just its precision. We characterize the selection exactly: for any parameter-free weighting-matrix map, the GMM estimand is a sum-to-one combination of instrument-specific Wald estimands, with closed-form weights and an exact non-negativity condition; efficient weighting adds a heterogeneity penalty. Continuously updated GMM exits this class through a variance-score remainder. Under positive regression dependence each Wald estimand is a convex combination of compliance-type LATEs, and under maintained validity a $J$-rejection indicates unequal Wald estimands rather than invalid instruments. We propose Representativeness Targeting (RT), which estimates a researcher-specified convex combination of the Wald estimands without imposing a common coefficient across moments; RT weights compliance types nonnegatively, attains the local asymptotic minimax bound for its target, and extends to unreachable policy targets via projection with identification-gap bounds. In Tennessee STAR, we find the $J$-test rejects the Wald-estimand equality while the heterogeneity penalty pulls the efficient-GMM estimate substantially below 2SLS; in a patent-leniency design, RT delivers a policy-relevant surrogate that standard GMM weightings miss.

Date: 2026-04, Revised 2026-09
New Economics Papers: this item is included in nep-ecm and nep-exp
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