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Sensitivity Analysis For Inference In 2SLS Estimation With Possibly-Flawes Instruments


  • Richard A. Ashley
  • Christopher F. Parmeter


Credible inference requires attention to the possible fragility of the results (p-values for key hypothesis tests) to flaws in the model assumptions, notably including 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. We provide a feasible sensitivity analysis of two-stage least squares 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 with an empirical application focusing on the education-earnings relationship.

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  • Richard A. Ashley & Christopher F. Parmeter, 2013. "Sensitivity Analysis For Inference In 2SLS Estimation With Possibly-Flawes Instruments," Working Papers e07-38, Virginia Polytechnic Institute and State University, Department of Economics.
  • Handle: RePEc:vpi:wpaper:e07-38

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    References listed on IDEAS

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      • Martin Lettau & Sydney C. Ludvigson, 2013. "Shocks and Crashes," NBER Chapters,in: NBER Macroeconomics Annual 2013, Volume 28, pages 293-354 National Bureau of Economic Research, Inc.
    6. Richard A. Ashley & Kwok Ping Tsang, 2014. "Credible Granger-Causality Inference with Modest Sample Lengths: A Cross-Sample Validation Approach," Econometrics, MDPI, Open Access Journal, vol. 2(1), pages 1-20, March.
    7. Christopher F Baum & Mark E. Schaffer & Steven Stillman, 2007. "Enhanced routines for instrumental variables/generalized method of moments estimation and testing," Stata Journal, StataCorp LP, vol. 7(4), pages 465-506, December.
    8. Richard Ashley & Kwok Ping Tsang & Randal J. Verbrugge, 2010. "Frequency Dependence in a Real-Time Monetary Policy Rule," Working Papers e07-21, Virginia Polytechnic Institute and State University, Department of Economics.
    9. Engelhardt, Gary V., 1996. "House prices and home owner saving behavior," Regional Science and Urban Economics, Elsevier, vol. 26(3-4), pages 313-336, June.
    10. Gonzalo, Jesus & Granger, Clive W J, 1995. "Estimation of Common Long-Memory Components in Cointegrated Systems," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(1), pages 27-35, January.
    11. Richard A. Ashley & Randall J. Verbrugge., 2006. "Mis-Specification in Phillips Curve Regressions: Quantifying Frequency Dependence in This Relationship While Allowing for Feedback," Working Papers e06-11, Virginia Polytechnic Institute and State University, Department of Economics.
    12. Richard Ashley & Randal Verbrugge, 2009. "Frequency Dependence in Regression Model Coefficients: An Alternative Approach for Modeling Nonlinear Dynamic Relationships in Time Series," Econometric Reviews, Taylor & Francis Journals, vol. 28(1-3), pages 4-20.
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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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    Robustness; invalid instruments; flawed instruments; instrumental variables; sensitivity analysis; two-stage least squares.;

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