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Robust Inference for Dyadic Data with Spatially Dependent Nodes

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  • Ulrich Hounyo
  • Jiahao Lin
  • Xiaojun Song

Abstract

We develop inference for complete dyadic samples with spatial dependence governed by geographic distances between nodes, covering settings beyond the scope of existing dyadic inference theory. We propose spatial variance and corrected block jackknife estimators consistent in nondegenerate and degenerate Gaussian cases. Under degeneracy, spatial subsampling consistently estimates possibly non-Gaussian limits. Combining the jackknife with subsampling yields the max jackknifes-ubsampling (MJS) interval, which provides pointwise asymptotically exact coverage in both Gaussian cases and conservative coverage in the specified non-Gaussian case. Fixed-effect extensions show that estimating node effects can change the fast limiting distribution. Simulations and an empirical illustration are provided.

Suggested Citation

  • Ulrich Hounyo & Jiahao Lin & Xiaojun Song, 2026. "Robust Inference for Dyadic Data with Spatially Dependent Nodes," Papers 2605.28349, arXiv.org, revised Oct 2026.
    • Handle: RePEc:arx:papers:2605.28349
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    References listed on IDEAS

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