Assessing Inference Methods

Fuente: arXiv
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Autor principal: Ferman, Bruno
Formato: Preprint
Publicado: 2019
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author Ferman, Bruno
author_facet Ferman, Bruno
contents We analyze different types of simulations that applied researchers can use to assess whether their inference methods reliably control false-positive rates. We show that different assessments involve trade-offs, varying in the types of problems they may detect, finite-sample performance, susceptibility to sequential-testing distortions, susceptibility to cherry-picking, and implementation complexity. We also show that a commonly used simulation to assess inference methods in shift-share designs can lead to misleading conclusions and propose alternatives. Overall, we provide novel insights and recommendations for applied researchers on how to choose, implement, and interpret inference assessments in their empirical applications.
format Preprint
id arxiv_https___arxiv_org_abs_1912_08772
institution arXiv
publishDate 2019
record_format arxiv
spellingShingle Assessing Inference Methods
Ferman, Bruno
Econometrics
We analyze different types of simulations that applied researchers can use to assess whether their inference methods reliably control false-positive rates. We show that different assessments involve trade-offs, varying in the types of problems they may detect, finite-sample performance, susceptibility to sequential-testing distortions, susceptibility to cherry-picking, and implementation complexity. We also show that a commonly used simulation to assess inference methods in shift-share designs can lead to misleading conclusions and propose alternatives. Overall, we provide novel insights and recommendations for applied researchers on how to choose, implement, and interpret inference assessments in their empirical applications.
title Assessing Inference Methods
topic Econometrics
url https://arxiv.org/abs/1912.08772