Nonseparable Dyadic Regression

Brice Romuald Gueyap Kounga

Papers from arXiv.org

Abstract: This paper studies a nonseparable model for dyadic outcomes, such as trade flows between pairs of countries, in which the outcome depends on the two agents' observed characteristics and on a scalar unobservable through an unknown function that is strictly increasing in the unobservable. I establish identification of a normalized representative of the structural function and of the error distribution, and propose kernel plug-in estimators of both. Because dyads sharing an agent are dependent, standard variance formulas fail: I derive a two-regime central limit theory in which a shared-agent variance component generically dominates, and propose an agent-level bootstrap valid in both regimes. Simulations show that independence-based confidence intervals undercover severely, while the bootstrap restores coverage.

Date: 2023-10, Revised 2026-08
New Economics Papers: this item is included in nep-ecm
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