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Computing moment inequality models using constrained optimization

Author

Listed:
  • Baiyu Dong
  • Yu-Wei Hsieh
  • Matthew Shum Caltech

Abstract

SummaryInference for moment inequality models is computationally demanding and often involves time-consuming grid search. By exploiting the equivalent formulations between unconstrained and constrained optimization, we establish new ways to compute the identified set and its confidence set in moment inequality models that overcome some of these computational hurdles. In simulations, using both linear and nonlinear moment inequality models, we show that our method significantly improves the solution quality and save considerable computing resources relative to conventional grid search. Our methods are user-friendly and can be implemented using a variety of canned software packages.

Suggested Citation

  • Baiyu Dong & Yu-Wei Hsieh & Matthew Shum Caltech, 2021. "Computing moment inequality models using constrained optimization," The Econometrics Journal, Royal Economic Society, vol. 24(3), pages 399-416.
    • Handle: RePEc:oup:emjrnl:v:24:y:2021:i:3:p:399-416.
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    File URL: http://hdl.handle.net/10.1093/ectj/utab014
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    Cited by:

    1. Vira Semenova, 2023. "Adaptive Estimation of Intersection Bounds: a Classification Approach," Papers 2303.00982, arXiv.org.
    2. Semenova, Vira, 2023. "Debiased machine learning of set-identified linear models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1725-1746.

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