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Sensitivity analysis for inference in 2SLS/GMM estimation with possibly flawed instruments

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  • Richard Ashley

  • Christopher Parmeter

Abstract

Credible inference requires attention to the possible fragility of the results ( $$p$$ p values for key hypothesis tests) to flaws in the model assumptions, notably accounting for the validity of the instruments used. Past sensitivity analysis has mainly consisted of experimentation with alternative model specifications and with tests of over-identifying restrictions which actually presuppose instrument validity. We provide a feasible sensitivity analysis of two-stage least-squares and GMM estimation, quantifying the fragility/robustness of inference with respect to possible flaws in the exogeneity assumptions made, and also indicating which of these assumptions are most crucial. The method is illustrated via application to a well-known study of the education–earnings relationship. Copyright Springer-Verlag Berlin Heidelberg 2015

Suggested Citation

  • Richard Ashley & Christopher Parmeter, 2015. "Sensitivity analysis for inference in 2SLS/GMM estimation with possibly flawed instruments," Empirical Economics, Springer, vol. 49(4), pages 1153-1171, December.
    • Handle: RePEc:spr:empeco:v:49:y:2015:i:4:p:1153-1171
    DOI: 10.1007/s00181-015-0916-0
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    References listed on IDEAS

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    Cited by:

    1. Kiviet, Jan F., 2016. "When is it really justifiable to ignore explanatory variable endogeneity in a regression model?," Economics Letters, Elsevier, vol. 145(C), pages 192-195.
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    3. Matthew A. Masten & Alexandre Poirier, 2021. "Salvaging Falsified Instrumental Variable Models," Econometrica, Econometric Society, vol. 89(3), pages 1449-1469, May.
    4. Di Giorno, Saverio & Dileo, Ivano & Busato, Francesco, 2024. "Shades of grand corruption among allocative efficiency and institutional settings. The case of Italy," Socio-Economic Planning Sciences, Elsevier, vol. 93(C).

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    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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