A New Bayesian Bootstrap for Quantitative Trade and Spatial Models

TL;DR

This paper introduces a novel Bayesian bootstrap method tailored for quantitative trade and spatial models, effectively addressing complex dependence, small sample sizes, and dual data roles in counterfactual analysis.

Contribution

It proposes a simple, robust Bayesian bootstrap procedure with finite-sample and asymptotic guarantees, improving uncertainty quantification in trade and spatial economic models.

Findings

Method provides finite-sample Bayesian and asymptotic frequentist guarantees.

Application to existing models demonstrates practical advantages.

Enhances uncertainty communication in counterfactual predictions.

Abstract

Economists use quantitative trade and spatial models to make counterfactual predictions. Because such predictions often inform policy decisions, it is important to communicate the uncertainty surrounding them. Three key challenges arise in this setting: the data are dyadic and exhibit complex dependence; the number of interacting units is typically small; and counterfactual predictions depend on the data in two distinct ways-through the estimation of structural parameters and through their role as inputs into the model's counterfactual equilibrium. I address these challenges by proposing a new Bayesian bootstrap procedure tailored to this context. The method is simple to implement and provides both finite-sample Bayesian and asymptotic frequentist guarantees. Revisiting the results in Waugh (2010), Caliendo and Parro (2015), and Artu\c{c} et al. (2010) illustrates the practical advantages of the approach.