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Regularization Based Anderson Rubin Tests for Many Instruments

Tchuente, Guy and Carrasco, Marine (2016) Regularization Based Anderson Rubin Tests for Many Instruments. Discussion paper. School of Economics, University of Kent (Unpublished) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:65076)

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https://ideas.repec.org/p/ukc/ukcedp/1608.html

Abstract

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)

Item Type: Monograph (Discussion paper)
Uncontrolled keywords: Many weak instruments; AR test; Bootstrap; Factor Model
Subjects: H Social Sciences
H Social Sciences > HA Statistics
Divisions: Divisions > Division of Human and Social Sciences > School of Economics
Depositing User: Guy Tchuente Nguembu
Date Deposited: 07 Dec 2017 12:49 UTC
Last Modified: 15 Sep 2021 15:23 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/65076 (The current URI for this page, for reference purposes)
Tchuente, Guy: https://orcid.org/0000-0001-8507-3337
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