Ulrich Hounyo, Jiahao Lin, Xiaojun Song
arXiv 27 May 2026 · Econometrics
arXiv:2605.28349 · PDF · DOI · OpenAlex · Extracted main text
Dyadic regression models are commonly analyzed under the conventional dyadic dependence framework, where two observations may be dependent only if the corresponding dyads share a node. This paper studies inference when nodes are ordered and nearby nodes are exposed to common latent shocks, so that dyads with no shared endpoint may still be dependent. Although each additional covariance term may be weak, the number of nearby-node dyad pairs grows with the sample size, making their aggregate contribution asymptotically non-negligible. We develop an inferential framework for dyadic arrays with ordered-node dependence and propose two variance estimators: a dependent-node dyadic cluster-robust variance estimator that retains covariance terms between dyads with nearby endpoints, and a row-column moving-block jackknife method that deletes adjacent blocks of nodes together with all dyads touching those nodes. We establish the asymptotic validity of both procedures under weak dependence along the ordered node index. Monte Carlo evidence shows improvements in size control, with the jackknife procedure displaying comparatively stable finite-sample performance. An application to international trade gravity regressions shows that accounting for ordered-node dependence substantially weakens the statistical evidence for free trade agreement effects.
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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 | Jochmans, Koen (2026) Two-Way Clustering with Non-Exchangeable Data | 0.941 | 6 | 4 | 83% |
| 2 | Chen, Kaicheng and Vogelsang, Timothy J (2024) Fixed-b asymptotics for panel models with two-way clustering | 0.843 | 3 | 3 | 100% |
| 3 | Chiang, Harold D. and Hansen, Bruce E. and Sasaki, Yuya (2024) Standard Errors for Two-Way Clustering with Serially Correlated Time Effects | 0.843 | 3 | 3 | 100% |
| 4 | Cameron, A Colin and Gelbach, Jonah B and Miller, Douglas L (2011) Robust inference with multiway clustering | 0.644 | 2 | 2 | 100% |
| 5 | Davezies, Laurent and D’Haultfœuille, Xavier and Guyonvarch, Yannick (2021) Empirical process results for exchangeable arrays | 0.644 | 2 | 2 | 100% |
| 6 | Davezies, Laurent and D'Haultfœuille, Xavier and Guyonvarch, Yannick (2025) Analytic inference with two-way clustering | 0.644 | 2 | 2 | 100% |
| 7 | MacKinnon, James G and Nielsen, Morten Ørregaard and Webb, Matthew D (2024) Jackknife inference with two-way clustering | 0.644 | 2 | 2 | 100% |
| 8 | Yoshihara, Ken-ichi (1976) Limiting behavior of U-statistics for stationary, absolutely regular processes | 0.511 | 4 | 2 | 25% |
| 9 | Hounyo, Ulrich and Lin, Jiahao (2025) Jackknife Variance Estimators for Two-Way Clustering with Serially Correlated Time Effects self | 0.511 | 2 | 2 | 50% |
| 10 | Aldous, David J (1981) Representations for partially exchangeable arrays of random variables | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 32 scored citations.