Chun Pang Chow, Hiroyuki Kasahara
arXiv 8 Apr 2026 · Econometrics
arXiv:2604.07131 · PDF · DOI · OpenAlex · Extracted main text
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.
appendix boundary found by appendix_command · 55% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Mogstad, Torgovitsky and Walters (2021) The Causal Interpretation of Two-Stage Least Squares with Multiple Instrumental Variables | 1.000 | 9 | 4 | 100% |
| 2 | Andrews, Chen and Tecchio (2025) The Purpose of an Estimator Is What It Does: Misspecification, Estimands, and Over-Identification | 1.000 | 6 | 3 | 100% |
| 3 | Goldsmith-Pinkham, Sorkin and Swift (2020) Bartik Instruments: What, When, Why, and How | 1.000 | 5 | 3 | 100% |
| 4 | Hall and Inoue (2003) The Large Sample Behaviour of the Generalized Method of Moments Estimator in Misspecified Models | 1.000 | 5 | 3 | 100% |
| 5 | Imbens and Angrist (1994) Identification and Estimation of Local Average Treatment Effects | 0.941 | 6 | 5 | 83% |
| 6 | Blandhol, Bonney, Mogstad and Torgovitsky (2022) When Is TSLS Actually LATE? | 0.928 | 5 | 5 | 80% |
| 7 | Mogstad, Santos and Torgovitsky (2018) Using Instrumental Variables for Inference about Policy Relevant Treatment Parameters | 0.899 | 11 | 5 | 73% |
| 8 | Heckman and Vytlacil (2005) Structural Equations, Treatment Effects, and Econometric Policy Evaluation | 0.874 | 5 | 2 | 100% |
| 9 | Farre-Mensa, Hegde and Ljungqvist (2020) What Is a Patent Worth? Evidence from the U.S. Patent “Lottery | 0.843 | 4 | 3 | 75% |
| 10 | Vytlacil (2002) Independence, Monotonicity, and Latent Index Models: An Equivalence Result | 0.843 | 3 | 3 | 100% |
Showing the top 10 of 36 scored citations.