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Computing moment inequality models using constrained optimization

Dong, Baiyu and Hsieh, Yu-Wei and Shum, Matthew (2021) Computing moment inequality models using constrained optimization. Econometrics Journal, 24 (3). pp. 399-416. ISSN 1368-4221. doi:10.1093/ectj/utab014. https://resolver.caltech.edu/CaltechAUTHORS:20220217-569318800

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Abstract

Inference 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.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1093/ectj/utab014DOIArticle
ORCID:
AuthorORCID
Shum, Matthew0000-0002-6262-915X
Additional Information:© 2021 Royal Economic Society. Published by Oxford University Press. Received: 05 June 2020; Accepted: 24 August 2020; Published: 03 May 2021.
Issue or Number:3
Classification Code:JEL: C01 - Econometrics; C51 - Model Construction and Estimation; C57 - Econometrics of Games and Auctions
DOI:10.1093/ectj/utab014
Record Number:CaltechAUTHORS:20220217-569318800
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20220217-569318800
Official Citation:Baiyu Dong, Yu-Wei Hsieh, Matthew Shum, Computing moment inequality models using constrained optimization, The Econometrics Journal, Volume 24, Issue 3, September 2021, Pages 399–416, https://doi.org/10.1093/ectj/utab014
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:113502
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:19 Feb 2022 00:39
Last Modified:03 Mar 2022 22:11

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