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

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  • Egger, Peter
  • Ji, Ting
  • Wang, Yulong

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.

Suggested Citation

  • Egger, Peter & Ji, Ting & Wang, Yulong, 2026. "PPML and Heavy-Tailed Trade and Factor Flows: Why Standard Inference Fails and How to Fix It," CEPR Discussion Papers 21998, Centre for Economic Policy Research.
    • Handle: RePEc:cpr:ceprdp:21998
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    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • F14 - International Economics - - Trade - - - Empirical Studies of Trade

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