AI 中文总结
本文指出PPML估计双边引力方程时传统推断因重尾失效，提出m-out-of-n自助法修正，在三个数据集中推翻原有显著结果。
AI 中文摘要
泊松伪最大似然（PPML）估计量被广泛用于估计双边引力方程。其一致性仅要求条件均值设定正确。然而，传统推断还要求得分具有有限方差且极限分布为高斯分布。我们证明这些条件并不成立：双边流动呈帕累托重尾分布，在结构性引力数据生成过程下，PPML 得分具有稳定极限分布，且三明治置信区间过窄。我们保留 PPML 用于点估计，但将三明治推断替换为对重尾稳健的 m-out-of-n 自助法。在三个双边数据场景中，修正幅度很大，并推翻了传统上显著的引力系数。
英文摘要
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.
