Working Paper: NBER ID: w31321
Authors: Rodrigo Ado; Arnaud Costinot; Dave Donaldson
Abstract: 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, Goldberg, Kennedy and Khandelwal (2020).
Keywords: quantitative modeling; trade war; causal predictions; goodness-of-fit measure
JEL Codes: C52; C68; E17; F10; R10
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| policy change (tariffs) (F13) | observed changes (O30) |
| other contemporaneous shocks (E39) | observed changes (O30) |
| model predictions (C59) | aggregate real income loss due to tariffs (F69) |
| IV-based goodness-of-fit measure (C26) | model predictions (C59) |
| model predictions (C59) | welfare loss (D69) |
| policy change (tariffs) (F13) | welfare loss (D69) |