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Improved Inference for the Instrumental Variables Estimator

Author

Listed:
  • Charles Nelson

    (University of Washington)

  • Richard Startz

    (University of Washington)

  • Eric Zivot

    (University of Washington)

Abstract

It is now well known that standard asymptotic inference techniques for instrumental variable estimation perform very poorly in the presence of weak instruments. Specifically, standard asymptotic techniques give spuriously small standard errors, leading investigators to accept apparently tight confidence regions which unfortunately may be very far from the true parameter of interest. We present an improved technique for inference on structural parameters based on reduced form estimates. The `S-statistic' produces confidence regions based on a joint test of the structural hypothesis and the identification condition. The S-statistic converges to the standard asymptotic Wald statistic as identification becomes certain, has much better size properties when the instruments are weak, and may be inverted in closed form to conveniently compute confidence regions. In addition to providing improved inference for instrumental variable estimation, the technique suggested here may be useful in other applications where weak identification is important.

Suggested Citation

  • Charles Nelson & Richard Startz & Eric Zivot, 2000. "Improved Inference for the Instrumental Variables Estimator," Econometric Society World Congress 2000 Contributed Papers 1600, Econometric Society.
  • Handle: RePEc:ecm:wc2000:1600
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Paul A. Bekker & Jan van der Ploeg, 2000. "Instrumental Variable Estimation Based on Grouped Data," Econometric Society World Congress 2000 Contributed Papers 1862, Econometric Society.
    2. Dufour, J.M., 2001. "Logique et tests d'hypotheses: reflexions sur les problemes mal poses en econometrie," Cahiers de recherche 2001-15, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    3. Benoit Perron, 2003. "Semiparametric Weak-Instrument Regressions with an Application to the Risk-Return Tradeoff," The Review of Economics and Statistics, MIT Press, pages 424-443.
    4. Kleibergen, Frank & Zivot, Eric, 2003. "Bayesian and classical approaches to instrumental variable regression," Journal of Econometrics, Elsevier, pages 29-72.
    5. 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.
    6. Dufour, Jean-Marie, 2001. "Logique et tests d’hypothèses," L'Actualité Economique, Société Canadienne de Science Economique, vol. 77(2), pages 171-190, juin.
    7. D. S. Poskitt & C. L. Skeels, 2004. "Approximating the Distribution of the Instrumental Variables Estimator when the Concentration Parameter is Small," Monash Econometrics and Business Statistics Working Papers 19/04, Monash University, Department of Econometrics and Business Statistics.
    8. D.S. Poskitt & C.L. Skeels, 2005. "Small Concentration Asymptotics and Instrumental Variables Inference," Department of Economics - Working Papers Series 948, The University of Melbourne.
    9. Jean-Marie Dufour & Mohamed Taamouti, 2005. "Projection-Based Statistical Inference in Linear Structural Models with Possibly Weak Instruments," Econometrica, Econometric Society, vol. 73(4), pages 1351-1365, July.
    10. Dollar, David & Kraay, Aart, 2003. "Institutions, trade, and growth : revisiting the evidence," Policy Research Working Paper Series 3004, The World Bank.
    11. D.S. Poskitt & C.L. Skeels, 2002. "Assessing Instrumental Variable Relevance:An Alternative Measure and Some Exact Finite Sample Theory," Department of Economics - Working Papers Series 862, The University of Melbourne.

    More about this item

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General

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