Susanne Schennach, Vincent Starck
arXiv 7 Nov 2025 · Econometrics · publishedEconometrica (2026) · 1 citations (OpenAlex)
arXiv:2511.05712 · PDF · DOI · OpenAlex · Extracted main text
We propose a novel optimal transport-based version of the Generalized Method of Moment (GMM). Instead of handling overidentification by reweighting the data to satisfy the moment conditions (as in Generalized Empirical Likelihood methods), this method proceeds by allowing for errors in the variables of the least mean-square magnitude necessary to simultaneously satisfy all moment conditions. This approach, based on the notions of optimal transport and Wasserstein metric, aims to address the problem of assigning a logical interpretation to GMM results even when overidentification tests reject the null, a situation that cannot always be avoided in applications. We illustrate the method by revisiting Duranton, Morrow and Turner's (2014) study of the relationship between a city's exports and the extent of its transportation infrastructure. Our results corroborate theirs under weaker assumptions and provide insight into the error structure of the variables.
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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 | W. Newey and R. J. Smith (2004) Higher-Order Properties of GMM and Generalized Empirical Likelihood Estimators | 0.928 | 4 | 3 | 100% |
| 2 | Masten, Matthew A and Poirier, Alexandre (2021) Salvaging falsified instrumental variable models | 0.894 | 7 | 4 | 71% |
| 3 | Duranton, Gilles and Morrow, Peter M and Turner, Matthew A (2014) Roads and Trade: Evidence from the US | 0.874 | 8 | 2 | 100% |
| 4 | W. Newey and D. McFadden Large Sample Estimation and Hypothesis Testing | 0.843 | 5 | 3 | 60% |
| 5 | F. Santambrogio Optimal Transport for Applied Mathematicians | 0.737 | 3 | 2 | 100% |
| 6 | C. Villani Optimal transport: Old and New | 0.737 | 3 | 2 | 100% |
| 7 | J. Blanchet and K. Murthy and N. Si Confidence regions inWasserstein distributionally robust estimation | 0.644 | 2 | 2 | 100% |
| 8 | A. Chesher The Effect of Measurement Error | 0.644 | 2 | 2 | 100% |
| 9 | T. G. Conley and C. B. Hansen and P. E. Rossi Plausibly Exogenous | 0.644 | 2 | 2 | 100% |
| 10 | T. Christensen and B. Connault Counterfactual Sensitivity and Robustness | 0.644 | 2 | 2 | 100% |
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