Nonparametric Identification of Models for Dyadic Data”

Paul Diegert and Koen Jochmans

No 24-1574, TSE Working Papers from Toulouse School of Economics (TSE)

Abstract: Consider dyadic random variables on units from a given population. It is common to assume that these variables are jointly exchangeable and dissociated. In this case they admit a non-separable specification with two-way unobserved heterogeneity. The analysis of this type of structure is of considerable interest but little is known about their nonparametric identifiability, especially when the unobserved heterogeneity is continuous. We provide conditions under which both the distribution of the observed random variables conditional on the unit-specific heterogeneity and the distribution of the unit-specific heterogeneity itself are uniquely recoverable from knowledge of the joint marginal distribution of the observable random variables alone without imposing parametric restrictions.

Keywords: Exchangeability; conditional independence; dyadic data; network; two-way; heterogeneity (search for similar items in EconPapers)
Date: 2024-09-17
New Economics Papers: this item is included in nep-ecm, nep-ipr and nep-net
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Related works:
Working Paper: Nonparametric Identification of Models for Dyadic Data (2025)
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Persistent link: https://EconPapers.repec.org/RePEc:tse:wpaper:129722

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