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Non-asymptotic inference in instrumental variables estimation

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  • Joel L. Horowitz

    (Institute for Fiscal Studies and Northwestern University)

Abstract

This paper presents a simple non-asymptotic method for carrying out inference in IV models. The method is a non-Studentized version of the Anderson-Rubin test but is motivated and analyzed differently. In contrast to the conventional Anderson-Rubin test, the method proposed here does not require restrictive distributional assumptions, linearity of the estimated model, or simultaneous equations. Nor does it require knowledge of whether the instruments are strong or weak. It does not require testing or estimating the strength of the instruments. The method can be applied to quantile IV models that may be nonlinear and can be used to test a parametric IV model against a nonparametric alternative. The results presented here hold in finite samples, regardless of the strength of the instruments.

Suggested Citation

  • Joel L. Horowitz, 2017. "Non-asymptotic inference in instrumental variables estimation," CeMMAP working papers CWP46/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  • Handle: RePEc:ifs:cemmap:46/17
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    References listed on IDEAS

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    Cited by:

    1. Zhu, Ying, 2018. "Concentration Based Inference in High Dimensional Generalized Regression Models (I: Statistical Guarantees)," MPRA Paper 88502, University Library of Munich, Germany.

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    More about this item

    Keywords

    Weak instruments; normal approximation; finite-sample bounds;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation

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