PPML and Heavy-Tailed Trade and Factor Flows: Why Standard Inference Fails and How to Fix It

Peter H. Egger; Ting Ji; Yulong Wang

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arXiv · 2609.18750

PPML and Heavy-Tailed Trade and Factor Flows: Why Standard Inference Fails and How to Fix It

Peter H. Egger·Ting Ji·Yulong Wang

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Abstract

The Poisson pseudo-maximum likelihood (PPML) estimator is widely used for estimating bilateral gravity equations. Its consistency requires only a correctly specified conditional mean. Conventional inference, however, also requires finite-variance scores and Gaussian limits. We show that these conditions fail: bilateral flows are Pareto-tailed, PPML scores have a stable limit under a structural gravity data-generating process, and sandwich confidence intervals are too narrow. We retain PPML for point estimation but replace sandwich inference with an m-out-of-n bootstrap robust to heavy tails. Across three bilateral data settings, the correction is large and overturns conventionally significant gravity coefficients.

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Peter H. Egger, Ting Ji, Yulong Wang. 2026-09-16. PPML and Heavy-Tailed Trade and Factor Flows: Why Standard Inference Fails and How to Fix It. https://arxiv.org/abs/2609.18750

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