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Jochmans, Koen
(2026)
Clustered Inference for Dyadic Data with Dependent Latent Positions.
TSE Working Paper, n. 26-1701, Toulouse
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Official URL : http://tse-fr.eu/pub/131300
Abstract
Inference procedures for dyadic data based on two-way clustering rely on the data being exchangeable and dissociated. In particular, observations must be independent if they have no index in common. In an effort to relax this we consider, instead, data where Yij and Ypq can be dependent for all index pairs, with the dependence vanishing as the distance between the indices grows large. We establish limit theory for the sample mean and propose analytical and bootstrap procedures to perform inference.
| Item Type: | Monograph (Working Paper) |
|---|---|
| Language: | English |
| Date: | 27 June 2026 |
| Place of Publication: | Toulouse |
| Uncontrolled Keywords: | bootstrap, clustering, dependence, dyadic data, inference, serial correlation |
| Subjects: | B- ECONOMIE ET FINANCE |
| Divisions: | TSE-R (Toulouse) |
| Institution: | Université Toulouse Capitole |
| Site: | UT1 |
| Date Deposited: | 22 Jan 2026 08:57 |
| Last Modified: | 13 Oct 2026 13:17 |
| OAI Identifier: | oai:tse-fr.eu:131300 |
| URI: | https://publications.ut-capitole.fr/id/eprint/51819 |
