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When is it really justifiable to ignore explanatory variable endogeneity in a regression model?

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  • Jan F. Kiviet

Abstract

A conversion of standard ordinary least-squares results into inference which is robust under endogeneity of some regressors has been put forward in Ashley and Parmeter, Economics Letters, 137 (2015) 70-74. However, their conversion is based on an incorrect (though by accident conservative) asymptotic approximation and entails a neglected but avoidable randomness. By a very basic example it is illustrated why a much more sophisticated asymptotic expansion under a stricter set of assumptions is required than used by these authors. Next, particular aspects of their consequently .awed sensitivity analysis for an empirical growth model are replaced by results based on a proper limiting distribution for a feasible inconsistency corrected least-squares estimator. Finally we provide references to literature where relevant asymptotic approximations have been derived which should enable to produce similar endogeneity robust inference for more general models and hypotheses than currently available.

Suggested Citation

  • Jan F. Kiviet, 2015. "When is it really justifiable to ignore explanatory variable endogeneity in a regression model?," UvA-Econometrics Working Papers 15-05, Universiteit van Amsterdam, Dept. of Econometrics.
  • Handle: RePEc:ame:wpaper:1505
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    References listed on IDEAS

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    1. Ashley, Richard A. & Parmeter, Christopher F., 2015. "When is it justifiable to ignore explanatory variable endogeneity in a regression model?," Economics Letters, Elsevier, vol. 137(C), pages 70-74.
    2. Jan F. Kiviet, 2013. "Identification and inference in a simultaneous equation under alternative information sets and sampling schemes," Econometrics Journal, Royal Economic Society, vol. 16(1), pages 24-59, February.
    3. Richard Ashley, 2009. "Assessing the credibility of instrumental variables inference with imperfect instruments via sensitivity analysis," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(2), pages 325-337, March.
    4. Kiviet Jan F., 2017. "Discriminating between (in)valid External Instruments and (in)valid Exclusion Restrictions," Journal of Econometric Methods, De Gruyter, vol. 6(1), pages 1-9, January.
    5. Aart Kraay, 2012. "Instrumental variables regressions with uncertain exclusion restrictions: a Bayesian approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(1), pages 108-128, January.
    6. N. Gregory Mankiw & David Romer & David N. Weil, 1992. "A Contribution to the Empirics of Economic Growth," The Quarterly Journal of Economics, Oxford University Press, vol. 107(2), pages 407-437.
    7. Small, Dylan S., 2007. "Sensitivity Analysis for Instrumental Variables Regression With Overidentifying Restrictions," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1049-1058, September.
    8. 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.
    9. Michael P. Murray, 2006. "Avoiding Invalid Instruments and Coping with Weak Instruments," Journal of Economic Perspectives, American Economic Association, vol. 20(4), pages 111-132, Fall.
    10. Peter Ebbes & Michel Wedel & Ulf Böckenholt, 2009. "Frugal IV alternatives to identify the parameter for an endogenous regressor," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(3), pages 446-468, April.
    11. Timothy G. Conley & Christian B. Hansen & Peter E. Rossi, 2012. "Plausibly Exogenous," The Review of Economics and Statistics, MIT Press, vol. 94(1), pages 260-272, February.
    12. 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.
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    Cited by:

    1. Jan F. Kiviet, 2016. "Testing the impossible: identifying exclusion restrictions," UvA-Econometrics Working Papers 16-03, Universiteit van Amsterdam, Dept. of Econometrics.
    2. Richard Ashley & Christopher F. Parmeter, 2018. "A Correction/Update to “When Is It Justifiable to Ignore Variable Endogeneity In A Regression Model?â€," Working Papers 2018-01, University of Miami, Department of Economics.

    More about this item

    JEL classification:

    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • O5 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies

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