Limit Theorems for Network Data without Metric Structure

Wen Jiang, Yachen Wang, Zeqi Wu and Xingbai Xu

Papers from arXiv.org

Abstract: This paper develops limit theorems for random variables with network dependence, without requiring that individuals in the network to be located in a Euclidean or metric space. This distinguishes our approach from most existing limit theorems in network econometrics, which are based on weak dependence concepts such as strong mixing, near-epoch dependence, and $\psi$-dependence. By relaxing the assumption of an underlying metric space, our theorems can be applied to a broader range of network data, including financial and social networks. To derive the limit theorems, we generalize the concept of functional dependence (also known as physical dependence) from time series to random variables with network dependence. Using this framework, we establish several inequalities, a law of large numbers, and central limit theorems. Furthermore, we verify the conditions for these limit theorems based on primitive assumptions for spatial autoregressive models, which are widely used in network data analysis.

Date: 2025-11
New Economics Papers: this item is included in nep-mac and nep-net
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://arxiv.org/pdf/2511.17928 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:2511.17928

Access Statistics for this paper

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