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Autore principale: Sanders, Bas
Natura: Preprint
Pubblicazione: 2023
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Accesso online:https://arxiv.org/abs/2311.14032
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author Sanders, Bas
author_facet Sanders, Bas
contents Counterfactuals in quantitative trade and spatial models are functions of the current state of the world and the model parameters. Common 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, and show that it is both practical and theoretically justified. I apply the proposed method to the settings in Adao, Costinot, and Donaldson (2017) and Allen and Arkolakis (2022) and find non-trivial uncertainty about counterfactuals.
format Preprint
id arxiv_https___arxiv_org_abs_2311_14032
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Measurement Error and Counterfactuals in Quantitative Trade and Spatial Models
Sanders, Bas
Econometrics
Counterfactuals in quantitative trade and spatial models are functions of the current state of the world and the model parameters. Common 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, and show that it is both practical and theoretically justified. I apply the proposed method to the settings in Adao, Costinot, and Donaldson (2017) and Allen and Arkolakis (2022) and find non-trivial uncertainty about counterfactuals.
title Measurement Error and Counterfactuals in Quantitative Trade and Spatial Models
topic Econometrics
url https://arxiv.org/abs/2311.14032