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ON THE POWER PROPERTIES OF INFERENCE FOR PARAMETERS WITH INTERVAL IDENTIFIED SETS

Published online by Cambridge University Press:  03 August 2026

Federico Bugni*
Affiliation:
Northwestern University, USA
Mengsi Gao
Affiliation:
University of Southern California, USA
Filip Obradović
Affiliation:
UCLA, USA
Amilcar Velez
Affiliation:
Cornell University, USA
*
Address correspondence to Federico Bugni, Northwestern University, United States, e-mail: federico.bugni@northwestern.edu
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Abstract

This article studies the power properties of confidence intervals (CIs) for a partially-identified parameter of interest with an interval identified set. We assume that the researcher has bounds estimators needed to construct the CIs proposed by Imbens and Manski (2004), Stoye (2009), and Stoye (2020), denoted by $CI_{\alpha }^{1}$, $CI_{\alpha }^{2}$, $CI_{\alpha }^{3}$, and $CI_{\alpha }^{4}$. We also assume that these bounds estimators are “ordered”: the lower bound estimator is less than or equal to the upper bound estimator. This setup arises in economic applications involving missing data and treatment effects. Under these conditions, we establish two results. First, we show that $CI_{\alpha }^{1}$ and $CI_{\alpha }^{2}$ are equally powerful, and both dominate $CI_{\alpha }^{3}$ and $CI_{\alpha }^{4}$. Second, we consider a favorable situation in which there are two possible bounds estimators to construct these CIs, and one is more efficient than the other. One would expect that the more efficient bounds estimator yields more powerful inference. We prove that this desirable result holds for $CI_{\alpha }^{1}$ and $CI_{\alpha }^{2}$, but not necessarily for $CI_{\alpha }^{3}$ or $CI_{\alpha }^{4}$. In summary, within the class of models considered, $CI_{\alpha }^{1}$ and $CI_{\alpha }^{2}$ have identical power properties, and both compare favorably to $CI_{\alpha }^{3}$ and $CI_{\alpha }^{4}$.

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Type
ARTICLE
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2026. Published by Cambridge University Press
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