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On Bootstrap Validity for the Test of Overidentifying Restrictions with Many Instruments and Heteroskedasticity

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  • Wang, Wenjie

Abstract

This note studies the asymptotic validity of bootstrapping the test of overidentifying restrictions under many/many weak instruments and heteroskedasticity. We show that the wild bootstrap consistently estimates the null limiting distributions of a jackknife overidentification statistic under this asymptotic framework. In particular, such bootstrap validity holds even when the bootstrap procedure fails to mimic well the distribution of the jackknife instrumental variable estimator, an important component of the statistic of interest. Monte Carlo simulations show that the wild bootstrap provides a more reliable method than that based on asymptotic critical values to approximate the null distributions of interest under many/many weak instruments and heteroskedasticity.

Suggested Citation

  • Wang, Wenjie, 2020. "On Bootstrap Validity for the Test of Overidentifying Restrictions with Many Instruments and Heteroskedasticity," MPRA Paper 104858, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:104858
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    References listed on IDEAS

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    Cited by:

    1. Wang, Wenjie, 2021. "Bootstrap Inference for Partially Linear Model with Many Regressors," MPRA Paper 106391, University Library of Munich, Germany.

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    More about this item

    Keywords

    Bootstrap; Overidentification Tests; Many Instruments; Weak Instruments; Heteroskedasticity;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation

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