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Sample sensitivity for two-step and continuous updating GMM estimators

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  • Onishi, Rikuto
  • Otsu, Taisuke

Abstract

This paper follows up the sensitivity analysis by Andrews, Gentzkow and Shapiro (2017) for biases in GMM estimators due to local violations of identifying assumptions, and proposes complementary bias measures that are sensitive to different choices of GMM weight matrices by considering a specific form of the local perturbation. Our method accommodates the two-step and continuous updating GMM estimators with or without centering. The proposed bias measures are illustrated by a consumption based asset pricing model using Japanese data.

Suggested Citation

  • Onishi, Rikuto & Otsu, Taisuke, 2021. "Sample sensitivity for two-step and continuous updating GMM estimators," Economics Letters, Elsevier, vol. 198(C).
    • Handle: RePEc:eee:ecolet:v:198:y:2021:i:c:s0165176520304456
    DOI: 10.1016/j.econlet.2020.109685
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    References listed on IDEAS

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    1. Whitney K. Newey & Richard J. Smith, 2004. "Higher Order Properties of Gmm and Generalized Empirical Likelihood Estimators," Econometrica, Econometric Society, vol. 72(1), pages 219-255, January.
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    3. Isaiah Andrews & Matthew Gentzkow & Jesse M. Shapiro, 2017. "Measuring the Sensitivity of Parameter Estimates to Estimation Moments," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 132(4), pages 1553-1592.
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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Sensitivity analysis; Generalized method of moments; Misspecification;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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