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

2026-09-16

AI Summary

该研究解决了PPML估计双边引力方程时标准推断失效的问题，通过使用m-out-of-n自助法来处理重尾数据，以获得更稳健的推断。

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.

Problem

• PPML
• Heavy-tailed
• Inference

Innovation

• Poisson pseudo-maximum likelihood (PPML)
• heavy-tailed data
• m-out-of-n bootstrap
• stable limit
• gravity model