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Detecting Lack Of Identification In Gmm

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  • Wright, Jonathan H.

Abstract

In the standard linear instrumental variables regression model, it must be assumed that the instruments are correlated with the endogenous variables in order to ensure the consistency and asymptotic normality of the usual instrumental variables estimator. Indeed, if the instruments are only slightly correlated with the endogenous variables, the conventional Gaussian asymptotic theory may still provide a very poor approximation to the finite sample distribution of the usual instrumental variables estimator. Because of the crucial role of this identification condition, it is common to test for instrument relevance by a first-stage F-test. Identification issues also arise in the generalized method of moments model, of which the linear instrumental variables model is a special case. But I know of no means, in the existing literature, of testing for identification in this model. This paper proposes a test of the null of underidentification in the generalized method of moments model.
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Suggested Citation

  • Wright, Jonathan H., 2003. "Detecting Lack Of Identification In Gmm," Econometric Theory, Cambridge University Press, vol. 19(02), pages 322-330, April.
  • Handle: RePEc:cup:etheor:v:19:y:2003:i:02:p:322-330_19
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    Cited by:

    1. Gospodinov, Nikolay & Kan, Raymond & Robotti, Cesare, 2017. "Too Good to Be True? Fallacies in Evaluating Risk Factor Models," FRB Atlanta Working Paper 2017-9, Federal Reserve Bank of Atlanta.
    2. Morris A. Davis & Jonas D. M. Fisher & Toni M. Whited, 2014. "Macroeconomic Implications of Agglomeration," Econometrica, Econometric Society, vol. 82(2), pages 731-764, March.
    3. Dufour, Jean-Marie & Taamouti, Mohamed, 2007. "Further results on projection-based inference in IV regressions with weak, collinear or missing instruments," Journal of Econometrics, Elsevier, vol. 139(1), pages 133-153, July.
    4. A. Craig Burnside, 2007. "Empirical Asset Pricing and Statistical Power in the Presence of Weak Risk Factors," NBER Working Papers 13357, National Bureau of Economic Research, Inc.
    5. Bravo, Francesco & Crudu, Federico, 2012. "Efficient bootstrap with weakly dependent processes," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3444-3458.
    6. Kleibergen, Frank & Paap, Richard, 2006. "Generalized reduced rank tests using the singular value decomposition," Journal of Econometrics, Elsevier, vol. 133(1), pages 97-126, July.
    7. repec:eee:econom:v:199:y:2017:i:1:p:49-62 is not listed on IDEAS
    8. Arellano, Manuel & Hansen, Lars Peter & Sentana, Enrique, 2012. "Underidentification?," Journal of Econometrics, Elsevier, vol. 170(2), pages 256-280.
    9. Matthijs Lof, 2014. "GMM Estimation with Non-causal Instruments under Rational Expectations," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(2), pages 279-286, April.
    10. Craig Burnside, 2016. "Identification and Inference in Linear Stochastic Discount Factor Models with Excess Returns," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 14(2), pages 295-330.
    11. Canova, Fabio & Sala, Luca, 2009. "Back to square one: Identification issues in DSGE models," Journal of Monetary Economics, Elsevier, vol. 56(4), pages 431-449, May.
    12. repec:eee:econom:v:201:y:2017:i:1:p:43-71 is not listed on IDEAS
    13. Strebulaev, Ilya A. & Whited, Toni M., 2012. "Dynamic Models and Structural Estimation in Corporate Finance," Foundations and Trends(R) in Finance, now publishers, vol. 6(1–2), pages 1-163, November.
    14. Dufour, Jean-Marie & Khalaf, Lynda & Kichian, Maral, 2006. "Inflation dynamics and the New Keynesian Phillips Curve: An identification robust econometric analysis," Journal of Economic Dynamics and Control, Elsevier, vol. 30(9-10), pages 1707-1727.
    15. Prosper Donovon & Alastair R. Hall, 2017. "The Asymptotic Properties of GMM and Indirect Inference under Second Inference," The School of Economics Discussion Paper Series 1705, Economics, The University of Manchester.
    16. Gregory Phelan & Alexis Akira Toda, 2015. "On the Robustness of Theoretical Asset Pricing Models," Department of Economics Working Papers 2015-10, Department of Economics, Williams College.
    17. Enrique Sentana, 2015. "Finite Underidentification," Working Papers wp2015_1508, CEMFI.
    18. Djankov, Simeon & Montalvo, Jose G. & Reynal-Querol, Marta, 2009. "Aid with multiple personalities," Journal of Comparative Economics, Elsevier, vol. 37(2), pages 217-229, June.
    19. Inoue, Atsushi & Rossi, Barbara, 2011. "Testing for weak identification in possibly nonlinear models," Journal of Econometrics, Elsevier, vol. 161(2), pages 246-261, April.

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