Out-of-sample gravity predictions and trade policy counterfactuals

Gravity equations are often used to evaluate the effects of trade policies, such as regional trade agreements. We argue that their suitability for this purpose critically depends on their ability to produce unbiased out-of-sample predictions. We propose a methodology to evaluate the out-of-sample predictions obtained with gravity equations and with machine learning methods. We find that the 3-way gravity model is difficult to beat when the purpose is to evaluate policy interventions, further cementing its position as the predominant tool for applied trade policy analysis. However, when the goal is to predict individual flows, machine learning methods can be preferable.