Abstract
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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 487-526 |
| Number of pages | 40 |
| Journal | Quantitative Economics |
| Volume | 10 |
| Issue number | 2 |
| DOIs | |
| State | Published - May 1 2019 |
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All Science Journal Classification (ASJC) codes
- Economics and Econometrics
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A more powerful subvector Anderson Rubin test in linear instrumental variables regression. / Guggenberger, Patrik; Kleibergen, Frank; Mavroeidis, Sophocles.
In: Quantitative Economics, Vol. 10, No. 2, 01.05.2019, p. 487-526.Research output: Contribution to journal › Article
TY - JOUR
T1 - A more powerful subvector Anderson Rubin test in linear instrumental variables regression
AU - Guggenberger, Patrik
AU - Kleibergen, Frank
AU - Mavroeidis, Sophocles
PY - 2019/5/1
Y1 - 2019/5/1
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 -