A more powerful subvector Anderson Rubin test in linear instrumental variables regression

We study subvector inference in the linear instrumental variables model assuming homoskedasticity but allowing for weak instruments. The subvector Anderson and Rubin (1949) test that uses chi square critical values with degrees of freedom reduced by the number of parameters not under test, proposed...

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Bibliographic Details
Published inQuantitative economics Vol. 10; no. 2; pp. 487 - 526
Main Authors Guggenberger, Patrik, Kleibergen, Frank, Mavroeidis, Sophocles
Format Journal Article
LanguageEnglish
Published New Haven, CT The Econometric Society 01.05.2019
John Wiley & Sons, Inc
Subjects
Asymptotic size
C12
C26
Development economics
Econometrics
Economic models
Eigenvalues
Hypotheses
Identification
linear IV regression
Power
Risk preferences
subvector inference
Values
weak instruments
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Summary:We study subvector inference in the linear instrumental variables model assuming homoskedasticity but allowing for weak instruments. The subvector Anderson and Rubin (1949) test that uses chi square critical values with degrees of freedom reduced by the number of parameters not under test, proposed by Guggenberger, Kleibergen, Mavroeidis, and Chen (2012), controls size but is generally conservative. We propose a conditional subvector Anderson and Rubin test that uses datadependent critical values that adapt to the strength of identification of the parameters not under test. This test has correct size and strictly higher power than the subvector Anderson and Rubin test by Guggenberger et al. (2012). We provide tables with conditional critical values so that the new test is quick and easy to use. Application of our method to a model of risk preferences in development economics shows that it can strengthen empirical conclusions in practice.
Bibliography:Supplementary Material
ISSN:1759-7331
1759-7323
1759-7331
DOI:10.3982/QE1116