Computing moment inequality models using constrained optimization

Baiyu Dong, Yu-Wei Hsieh and Matthew Shum Caltech

The Econometrics Journal, 2021, vol. 24, issue 3, 399-416

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.

Keywords: Moment inequality; constrained optimization; MPEC; MPCC; partial identification (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:oup:emjrnl:v:24:y:2021:i:3:p:399-416.

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