Robust Inference for Dyadic Data with Spatially Dependent Nodes

Ulrich Hounyo, Jiahao Lin and Xiaojun Song

Papers from arXiv.org

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.

Date: 2026-05, Revised 2026-10
New Economics Papers: this item is included in nep-net
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://arxiv.org/pdf/2605.28349 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2605.28349

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().