TY - JOUR
T1 - A more powerful subvector Anderson Rubin test in linear instrumental variables regression
AU - Guggenberger, Patrik
AU - Kleibergen, Frank
AU - Mavroeidis, Sophocles
N1 - Funding Information:
Patrik Guggenberger: pxg27@psu.edu Frank Kleibergen: F.R.Kleibergen@uva.nl Sophocles Mavroeidis: sophocles.mavroeidis@economics.ox.ac.uk Guggenberger gratefully acknowledges the research support of the National Science Foundation via Grant SES-1326827. Mavroeidis gratefully acknowledges the research support of the European Research Council via Consolidator Grant 647152. The authors thank Ulrich Müller, seminar participants at various institutions, and three anonymous referees, as well as Rogan Feng, Julius Koll, Lewis McLean, and Jin Thed for research assistance. 1See, for example, Nelson and Startz (1990), Staiger and Stock (1997), Kleibergen (2002), Moreira (2003), Andrews, Moreira, and Stock (2006, 2008), Chernozhukov, Hansen, and Jansson (2009), Hillier (2009a, 2009b), and Andrews, Marmer, and Yu (2019).
Publisher Copyright:
Copyright © 2019 The Authors.
PY - 2019/5
Y1 - 2019/5
N2 - 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 data-dependent 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.
AB - 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 data-dependent 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.
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U2 - 10.3982/QE1116
DO - 10.3982/QE1116
M3 - Article
AN - SCOPUS:85065446978
VL - 10
SP - 487
EP - 526
JO - Quantitative Economics
JF - Quantitative Economics
SN - 1759-7323
IS - 2
ER -