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

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  • Kiviet, Jan F.

Abstract

A procedure that aims to pinpoint the sensitivity of ordinary least-squares based inferences regarding the degree of endogeneity of some regressors has been put forward in Ashley and Parmeter (2015a). Here it is demonstrated that this procedure is based on an incorrect and systematically too optimistic asymptotic approximation to the variance of inconsistent least-squares. Therefore, and because the suggested sensitivity findings pertain to a random set of estimated endogeneity correlations, the claimed significance levels are misleading. For a very basic one coefficient model it is demonstrated why much more sophisticated asymptotic expansions under a stricter set of assumptions are required. This enables to replace some of the flawed earlier sensitivity analysis results for an empirical growth model by asymptotically valid findings.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:ecolet:v:145:y:2016:i:c:p:192-195
    DOI: 10.1016/j.econlet.2016.06.021
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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. 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.
    6. 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.
    7. 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.
    8. 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.
    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. 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.
    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. 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.
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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.

    More about this item

    Keywords

    Sensitivity analysis; Simultaneity; Asymptotic expansions; Least-squares; Growth regression;

    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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