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

Author

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  • Patrik Guggenberger
  • Frank Kleibergen
  • Sophocles Mavroeidis

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.

Suggested Citation

  • Patrik Guggenberger & Frank Kleibergen & Sophocles Mavroeidis, 2019. "A more powerful subvector Anderson Rubin test in linear instrumental variables regression," Quantitative Economics, Econometric Society, vol. 10(2), pages 487-526, May.
    • Handle: RePEc:wly:quante:v:10:y:2019:i:2:p:487-526
    DOI: 10.3982/QE1116
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    Citations

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    Cited by:

    1. Eric Gautier & Christiern Rose, 2022. "Fast, Robust Inference for Linear Instrumental Variables Models using Self-Normalized Moments," Papers 2211.02249, arXiv.org, revised Nov 2022.
    2. Peter C. B. Phillips, 2022. "An Econometrician amongst Statisticians: T. W. Anderson," Cowles Foundation Discussion Papers 2333, Cowles Foundation for Research in Economics, Yale University.
    3. Kiviet, Jan, 2019. "Instrument-free inference under confined regressor endogeneity; derivations and applications," MPRA Paper 96839, University Library of Munich, Germany.
    4. Andrews, Donald W.K. & Cheng, Xu & Guggenberger, Patrik, 2020. "Generic results for establishing the asymptotic size of confidence sets and tests," Journal of Econometrics, Elsevier, vol. 218(2), pages 496-531.
    5. Xu Cheng & Winston Wei Dou & Zhipeng Liao, 2022. "Macro‐Finance Decoupling: Robust Evaluations of Macro Asset Pricing Models," Econometrica, Econometric Society, vol. 90(2), pages 685-713, March.
    6. Kiviet, Jan F., 2023. "Instrument-free inference under confined regressor endogeneity and mild regularity," Econometrics and Statistics, Elsevier, vol. 25(C), pages 1-22.
    7. Bun, Maurice J.G. & Kleibergen, Frank, 2022. "Identification Robust Inference For Moments-Based Analysis Of Linear Dynamic Panel Data Models," Econometric Theory, Cambridge University Press, vol. 38(4), pages 689-751, August.
    8. Guggenberger, Patrik & Kleibergen, Frank & Mavroeidis, Sophocles, 2023. "A test for Kronecker Product Structure covariance matrix," Journal of Econometrics, Elsevier, vol. 233(1), pages 88-112.
    9. Kees Jan van Garderen & Noud van Giersbergen, 2020. "A Nearly Similar Powerful Test for Mediation," Papers 2012.11342, arXiv.org, revised Jan 2022.
    10. Wang, Wenjie, 2020. "On the inconsistency of nonparametric bootstraps for the subvector Anderson–Rubin test," Economics Letters, Elsevier, vol. 191(C).
    11. Kleibergen, Frank, 2021. "Efficient size correct subset inference in homoskedastic linear instrumental variables regression," Journal of Econometrics, Elsevier, vol. 221(1), pages 78-96.
    12. Patrik Guggenberge & Frank Kleibergen & Sophocles Mavroeidis, 2021. "A Powerful Subvector Anderson Rubin Test in Linear Instrumental Variables Regression with Conditional Heteroskedasticity," Economics Series Working Papers 960, University of Oxford, Department of Economics.
    13. Frank Kleibergen & Lingwei Kong, 2023. "Identification Robust Inference for the Risk Premium in Term Structure Models," Papers 2307.12628, arXiv.org.
    14. Frank Kleibergen & Zhaoguo Zhan, 2021. "Double robust inference for continuous updating GMM," Papers 2105.08345, arXiv.org.
    15. Wang, Wenjie, 2021. "Wild Bootstrap for Instrumental Variables Regression with Weak Instruments and Few Clusters," MPRA Paper 106227, University Library of Munich, Germany.
    16. Horowitz, Joel L., 2021. "Bounding the difference between true and nominal rejection probabilities in tests of hypotheses about instrumental variables models," Journal of Econometrics, Elsevier, vol. 222(2), pages 1057-1082.

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