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A ridge-regularized jackknifed Anderson-Rubin test

Abstract:
We consider hypothesis testing in instrumental variable regression models with few included exogenous covariates but many instruments—possibly more than the number of observations. We show that a ridge-regularized version of the jackknifed Anderson and Rubin (henceforth AR) test controls asymptotic size in the presence of heteroscedasticity, and when the instruments may be arbitrarily weak. Asymptotic size control is established under weaker assumptions than those imposed for recently proposed jackknifed AR tests in the literature. Furthermore, ridge-regularization extends the scope of jackknifed AR tests to situations in which there are more instruments than observations. Monte Carlo simulations indicate that our method has favorable finite-sample size and power properties compared to recently proposed alternative approaches in the literature. An empirical application on the elasticity of substitution between immigrants and natives in the United States illustrates the usefulness of the proposed method for practitioners.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1080/07350015.2023.2290739

Authors


More by this author
Institution:
University of Oxford
Division:
SSD
Department:
Economics
Role:
Author
More by this author
Institution:
University of Oxford
Division:
SSD
Department:
Economics
Oxford college:
University College
Role:
Author
ORCID:
0000-0002-8851-8044


Publisher:
Taylor & Francis
Journal:
Journal of Business & Economic Statistics More from this journal
Volume:
42
Issue:
3
Pages:
1083-1094
Publication date:
2024-01-05
Acceptance date:
2023-11-01
DOI:
EISSN:
1537-2707
ISSN:
0735-0015


Language:
English
Keywords:
Pubs id:
1602404
Local pid:
pubs:1602404
Deposit date:
2024-03-11

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