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The primary motivation behind quantitative modeling in international trade and many other fields is to shed light on the economic consequences of policy changes. To help assess and potentially strengthen the credibility of such quantitative predictions we introduce an IV-based goodness-of-fit measure that provides the basis for testing causal predictions in arbitrary general-equilibrium environments as well as for estimating the average misspecification in these predictions. As an illustration of how to use our IV-based goodness-of-fit measure in practice, we revisit the welfare consequences of Trump’s trade war predicted by Fajgelbaum et al. (2020).
