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Simple adaptive size-exact testing for full-vector and subvector inference in moment inequality models

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  • Xiaoxia Shi

    (University of Wisconsin, Madison)

Abstract

We propose a simple test for moment inequalities that has exact size in normal models with known variance and has uniformly asymptotically exact size under asymptotic normality. The test compares the quasi-likelihood ratio statistic to a chi-squared critical value, where the degree of freedom is the rank of the inequalities that are active in finite samples. The test requires no simulation and thus is computationally fast and especially suitable for constructing confidence sets for parameters by test inversion. It uses no tuning parameter for moment selection and yet still adapts to the slackness of the moment inequalities. Furthermore, we show how the test can be easily adapted to inference on subvectors in the common empirical setting of conditional moment inequalities with nuisance parameters entering linearly. User-friendly Matlab code to implement the test is provided.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Xiaoxia Shi, 2022. "Simple adaptive size-exact testing for full-vector and subvector inference in moment inequality models," Economics Virtual Symposium 2022 09, Stata Users Group.
    • Handle: RePEc:boc:econ22:09
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    Cited by:

    1. Alexandre Poirier & Tymon S{l}oczy'nski, 2024. "Quantifying the Internal Validity of Weighted Estimands," Papers 2404.14603, arXiv.org, revised Oct 2025.
    2. Tersoo David Iorngurum, 2025. "The exchange rate pass‐through to domestic prices: A meta‐analysis," Journal of Economic Surveys, Wiley Blackwell, vol. 39(3), pages 1092-1124, July.
    3. David M Kaplan & Wei Zhao, 2023. "Comparing latent inequality with ordinal data," The Econometrics Journal, Royal Economic Society, vol. 26(2), pages 189-214.
    4. Isaiah Andrews & Jonathan Roth & Ariel Pakes, 2023. "Inference for Linear Conditional Moment Inequalities," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 90(6), pages 2763-2791.
    5. Matej Opatrny & Tomas Havranek & Zuzana Irsova & Milan Scasny, 2023. "Publication Bias and Model Uncertainty in Measuring the Effect of Class Size on Achievement," Working Papers IES 2023/19, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised May 2023.
    6. Hsieh, Yu-Wei & Shi, Xiaoxia & Shum, Matthew, 2022. "Inference on estimators defined by mathematical programming," Journal of Econometrics, Elsevier, vol. 226(2), pages 248-268.
    7. Leonard Goff & Eric Mbakop, 2025. "Inference on the value of a linear program," Papers 2506.06776, arXiv.org, revised Jun 2025.
    8. Shuowen Chen & Hiroaki Kaido, 2022. "Robust Tests of Model Incompleteness in the Presence of Nuisance Parameters," Papers 2208.11281, arXiv.org, revised Sep 2023.
    9. Abel Brodeur & Nikolai M. Cook & Jonathan S. Hartley & Anthony Heyes, 2024. "Do Preregistration and Preanalysis Plans Reduce p-Hacking and Publication Bias? Evidence from 15,992 Test Statistics and Suggestions for Improvement," Journal of Political Economy Microeconomics, University of Chicago Press, vol. 2(3), pages 527-561.
    10. Turansick, Christopher, 2025. "An alternative approach for nonparametric analysis of random utility models," Journal of Economic Theory, Elsevier, vol. 226(C).
    11. Evan K. Rose & Yotam Shem-Tov, 2021. "On Recoding Ordered Treatments as Binary Indicators," Papers 2111.12258, arXiv.org, revised Mar 2024.

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