Nonseparable Dyadic Regression

Brice Romuald Gueyap Kounga

Papers from arXiv.org

Abstract: This paper studies a nonseparable model for dyadic outcomes, such as bilateral trade flows, in which the outcome depends on both agents' characteristics and on a scalar unobservable through an unknown function increasing in that unobservable. I establish identification of a normalized structural function and the error distribution, propose kernel plug-in estimators, and derive a two-regime central limit theory under dyadic dependence in which a shared-agent variance component generically dominates. An agent-level bootstrap is proved consistent in that regime. Simulations show independence-based intervals undercover severely while the bootstrap substantially improves coverage. In bilateral trade data, conditional dispersion falls by more than half between small and large exporters.

Date: 2023-10, Revised 2026-09
New Economics Papers: this item is included in nep-ecm
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