Clustered Inference for Dyadic Data with Dependent Latent Positions

Koen Jochmans

No 26-1701, TSE Working Papers from Toulouse School of Economics (TSE)

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.

Keywords: bootstrap; clustering; dependence; dyadic data; inference; serial correlation (search for similar items in EconPapers)
Date: 2026-01-21, Revised 2026-06
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Working Paper: Clustered Inference for Dyadic Data with Dependent Latent Positions (2026)
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