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Counterfactual Sensitivity in Quantitative Spatial Models

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  • Bas Sanders

Abstract

Counterfactuals in quantitative spatial models are functions of the current state of the world and the model parameters. Current practice treats the current state of the world as perfectly observed, but there is good reason to believe that it is measured with error. This paper provides tools for quantifying uncertainty about counterfactuals when the current state of the world is measured with error. I recommend an empirical Bayes approach to uncertainty quantification, which is both practical and theoretically justified. I apply the proposed method to the applications in Adao, Costinot, and Donaldson (2017) and Allen and Arkolakis (2022) and find non-trivial uncertainty about counterfactuals.

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  • Bas Sanders, 2023. "Counterfactual Sensitivity in Quantitative Spatial Models," Papers 2311.14032, arXiv.org, revised Mar 2024.
    • Handle: RePEc:arx:papers:2311.14032
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    References listed on IDEAS

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    1. Rodrigo Adao & Arnaud Costinot & Dave Donaldson, 2017. "Nonparametric Counterfactual Predictions in Neoclassical Models of International Trade," American Economic Review, American Economic Association, vol. 107(3), pages 633-689, March.
    2. Robert Dekle & Jonathan Eaton & Samuel Kortum, 2008. "Global Rebalancing with Gravity: Measuring the Burden of Adjustment," IMF Staff Papers, Palgrave Macmillan, vol. 55(3), pages 511-540, July.
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