Bias and consistency in three-way gravity models

We study the incidental parameter problem for the three-way Poisson Pseudo-Maximum Likelihood (PPML) estimator recently recommended for identifying the effects of trade policies and in other panel data gravity settings. Despite the number and variety of fixed effects involved, we confirm PPML is consistent for fixed T and we show it is in fact the only estimator among a wide range of PML gravity estimators that is generally consistent in this context when T is fixed. At the same time, asymptotic confidence intervals in fixed -T panels are not correctly centered at the true parameter values, and cluster-robust variance estimates used to construct standard errors are generally biased as well. We characterize each of these biases analytically and show both numerically and empirically that they are salient even for real-data settings with a large number of countries. We also offer practical remedies that can be used to obtain more reliable inferences of the effects of trade policies and other time-varying gravity variables, which we make available via an accompanying Stata package called ppml_fe_bias. (c) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Automated summary

This study examines the incidental parameter problem for the three-way Poisson Pseudo-Maximum Likelihood (PPML) estimator in fixed-T settings, confirming its consistency while noting biases in confidence intervals and variance estimates. Practical remedies are proposed to improve the reliability of inferences in trade policies and time-varying gravity variables.