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Robust Inference for Convex Pairwise Difference Estimators

Matias D. Cattaneo, Michael Jansson, Kenichi Nagasawa

arXiv 7 Oct 2025 · Econometrics

arXiv:2510.05991 · PDF · DOI · OpenAlex · Extracted main text

Abstract

This paper develops distribution theory and bootstrap-based inference methods for a broad class of convex pairwise difference estimators. These estimators minimize a kernel-weighted convex-in-parameter function over observation pairs that are similar in terms of certain covariates, where the similarity is governed by a localization (bandwidth) parameter. While classical results establish asymptotic normality under restrictive bandwidth conditions, we show that valid Gaussian and bootstrap-based inference remains possible under substantially weaker assumptions. First, we extend the theory of small bandwidth asymptotics to convex pairwise estimation settings, deriving robust Gaussian approximations even when a smaller than standard bandwidth is used. Second, we employ a debiasing procedure based on generalized jackknifing to enable inference with larger bandwidths, while preserving convexity of the objective function. Third, we construct a novel bootstrap method that adjusts for bandwidth-induced variance distortions, yielding valid inference across a wide range of bandwidth choices. Our proposed inference method enjoys demonstrable more robustness, while retaining the practical appeal of convex pairwise difference estimators.

Citation extraction

31
references
45
in-text mentions
31
distinct cited
10
self-citations
12,463
main-text words

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Most heavily cited references

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.

ReferenceIntensityMentionsSectionsMain text
1Honoré, B. E. and J. L. Powell (2005) Pairwise Difference Estimators for Nonlinear Models, in1.00053100%
2Hjort, N. L. and D. Pollard (1993) Asymptotics for Minimisers of Convex Processes0.87452100%
3Cattaneo, M. D., R. K. Crump, and M. Jansson (2014) b): bBootstrapping Density-Weighted Average Derivatives self0.73732100%
4Aradillas-Lopez, A., B. E. Honoré, and J. L. Powell (2007) Pairwise Difference Estimation with Nonparametric Control Variables0.64422100%
5Cattaneo, M. D., M. H. Farrell, M. Jansson, and R. P. Masini (2025) a): Higher-Order Refinements of Small Bandwidth Asymptotics for Density-Weighted Average Derivative Estimators self0.64422100%
6Powell, J. L (1994) Estimation of Semiparametric Models, in0.64422100%
7Heyde, C. C. and B. M. Brown (1970) On the Departure from Normality of a Certain Class of Martingales0.51121100%
8Ahn, H., H. Ichimura, J. L. Powell, and P. A. Ruud (2018) Simple Estimators for Invertible Index Models0.40511100%
9Ahn, H. and J. L. Powell (1993) Semiparametric Estimation of Censored Selection Models with a Nonparametric Selection Mechanism0.40511100%
10Aradillas-Lopez, A (2012) Pairwise-Difference Estimation of Incomplete Information Games0.40511100%

Showing the top 10 of 31 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Asymptotic Variance Theory for Trimmed Least Squares and Trimmed Least Absolute Deviations in Censored Panel Models with Fixed Effects0.51121