Regularization Based Anderson Rubin Tests for Many Instruments
Marine Carrasco () and
Guy Tchuente ()
Studies in Economics from School of Economics, University of Kent
This paper studies the asymptotic validity of the regularized Anderson Rubin (AR) tests in linear models with large number of instruments. The regularized AR tests use informationreduction methods to provide robust inference in instrumental variable (IV) estimation for data rich environments. We derive the asymptotic properties of the tests. Their asymptotic distribution depend on unknown nuisance parameters. A bootstrap method is used to obtain more reliable inference. The regularized tests are robust to many moment conditions in the sense that they are valid for both few and many instruments, and even for more instruments than the sample size. Our simulations show that the proposed AR tests work well and have better 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 the Elasticity of Intertemporal Substitution (EIS).
Keywords: Many weak instruments; AR test; Bootstrap; Factor Model (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5) Track citations by RSS feed
Downloads: (external link)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:ukc:ukcedp:1608
Ordering information: This working paper can be ordered from
Access Statistics for this paper
More papers in Studies in Economics from School of Economics, University of Kent School of Economics, University of Kent, Canterbury, Kent, CT2 7FS.
Bibliographic data for series maintained by Dr Anirban Mitra ().