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Nonparametric Identification of Models for Dyadic Data”

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  • Diegert, Paul
  • Jochmans, Koen

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.

Suggested Citation

  • Diegert, Paul & Jochmans, Koen, 2024. "Nonparametric Identification of Models for Dyadic Data”," TSE Working Papers 24-1574, Toulouse School of Economics (TSE).
    • Handle: RePEc:tse:wpaper:129722
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Exchangeability; conditional independence; dyadic data; network; two-way; heterogeneity;
    All these keywords.

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