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- W3146761808 abstract "This paper studies the asymptotic validity of the regularized Anderson Rubin (AR) testsin linear models with large number of instruments. The regularized AR tests use informationreductionmethods to provide robust inference in instrumental variable (IV) estimation fordata rich environments. We derive the asymptotic properties of the tests. Their asymptoticdistribution depend on unknown nuisance parameters. A bootstrap method is used to obtainmore reliable inference. The regularized tests are robust to many moment conditions in thesense that they are valid for both few and many instruments, and even for more instrumentsthan the sample size. Our simulations show that the proposed AR tests work well and havebetter performance than competing AR tests when the number of instruments is very large.The usefulness of the regularized tests is shown by proposing confidence intervals for theElasticity of Intertemporal Substitution (EIS)" @default.
- W3146761808 created "2021-04-13" @default.
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- W3146761808 date "2016-09-01" @default.
- W3146761808 modified "2023-09-23" @default.
- W3146761808 title "Regularization Based Anderson Rubin Tests for Many Instruments" @default.
- W3146761808 hasPublicationYear "2016" @default.
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