Nonseparable Dyadic Regression

Brice Romuald Gueyap Kounga

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arXiv · 2310.12825

Nonseparable Dyadic Regression

Brice Romuald Gueyap Kounga

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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.

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BibTeX RIS

Brice Romuald Gueyap Kounga. 2023-10-19. Nonseparable Dyadic Regression. https://arxiv.org/abs/2310.12825

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