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Rate-Agnostic Wald Inference for Dyadic Regressions

Benjamin O. Harrison, David T. Jacho-Chavez

arXiv 15 Sep 2026 · Econometrics

arXiv:2609.16968 · PDF · Extracted main text

Abstract

This paper develops Wald inference for least-squares estimation of linear regression models on dyadic data, accommodating configurations where multiple observations share the same pair of units (e.g., directed flows, multilayer networks, and dyadic panels). We establish that the dyadic-robust Wald statistic is asymptotically $χ^2_q$ for an arbitrary nonrandom sequence of full-rank restrictions, under a single condition on the accumulation of dependence. Throughout, no convergence rate is assumed or estimated, permitting the condition number of the score's variance matrix to diverge. We further propose a delete-one-unit jackknife alternative that is positive semidefinite by construction. This jackknife statistic attains the same asymptotic limit under one additional condition on dyad multiplicity and remains asymptotically conservative when that condition fails. A supplement contains all proofs, Monte Carlo experiments featuring estimated coefficients that converge at heterogeneous rates, and an empirical gravity application to bilateral trade.

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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
1Hansen, Bruce E. and Seojeong Lee (2019) Asymptotic theory for clustered samples1.00074100%
2Tabord-Meehan, Max (2019) Inference With Dyadic Data: Asymptotic Behavior of the Dyadic-Robust $t$-Statistic0.9619389%
3Graham, Bryan S (2020) Network data0.92843100%
4Santos Silva, J. M. C. and Silvana Tenreyro (2006) The Log of Gravity0.6936333%
5Aronow, Peter M., Cyrus Samii, and Valentina A. Assenova (2015) Cluster-Robust Variance Estimation for Dyadic Data0.64422100%
6Fafchamps, Marcel and Flore Gubert (2007) The formation of risk sharing networks0.64422100%
7Lin, Qiaohui, Robert Lunde, and Purnamrita Sarkar (2020) On the Theoretical Properties of the Network Jackknife0.64422100%
8Cameron, A. Colin, Jonah B. Gelbach, and Douglas L. Miller (2011) Robust Inference With Multiway Clustering0.5114225%
9Janson, Svante (1988) Normal Convergence by Higher Semiinvariants with Applications to Sums of Dependent Random Variables and Random Graphs0.5112250%
10Bell, Robert M. and Daniel F. McCaffrey (2002) Bias reduction in standard errors for linear regression with multi-stage samples0.40511100%

Showing the top 10 of 17 scored citations.