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Wild bootstrap tests for IV regression

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  • Russell Davidson

    ()
    (GREQAM - Groupement de Recherche en Économie Quantitative d'Aix-Marseille - Université de la Méditerranée - Aix-Marseille II - Université Paul Cézanne - Aix-Marseille III - Ecole des Hautes Etudes en Sciences Sociales (EHESS) - CNRS : UMR6579, CIREG - Centre interuniversitaire de recherche en économie quantitative - Université de Montréal, Department of Economics, McGill University - McGill University)

  • James Mackinnon

    ()
    (Department of Economics - Queen's University, Kingston, Ontario)

Abstract

We propose a wild bootstrap procedure for linear regression models estimated by instrumental variables. Like other bootstrap procedures that we have proposed elsewhere, it uses efficient estimates of the reduced-form equation(s). Unlike them, it takes account of possible heteroskedasticity of unknown form. We apply this procedure to t tests, including heteroskedasticity-robust t tests, and to the Anderson-Rubin test. We provide simulation evidence that it works far better than older methods, such as the pairs bootstrap. We also show how to obtain reliable confidence intervals by inverting bootstrap tests. An empirical example illustrates the utility of these procedures.

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Bibliographic Info

Paper provided by HAL in its series Working Papers with number halshs-00443550.

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Date of creation: 30 Dec 2009
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Handle: RePEc:hal:wpaper:halshs-00443550

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Related research

Keywords: Instrumental variables estimation; two-stage least squares; weak instruments; wild bootstrap; pairs bootstrap; residual bootstrap; confidence intervals; Anderson-Rubin test;

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References

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  1. JAMES G. MacKINNON, 2006. "Bootstrap Methods in Econometrics," The Economic Record, The Economic Society of Australia, vol. 82(s1), pages S2-S18, 09.
  2. Frank Kleibergen, 2002. "Pivotal Statistics for Testing Structural Parameters in Instrumental Variables Regression," Econometrica, Econometric Society, vol. 70(5), pages 1781-1803, September.
  3. Marcelo J. Moreira & Jack R. Porter & Gustavo A. Suarez, 2004. "Bootstrap and Higher-Order Expansion Validity When Instruments May Be Weak," Harvard Institute of Economic Research Working Papers 2048, Harvard - Institute of Economic Research.
  4. Douglas Staiger & James H. Stock, 1997. "Instrumental Variables Regression with Weak Instruments," Econometrica, Econometric Society, vol. 65(3), pages 557-586, May.
  5. Emmanuel Flachaire, 2001. "The Wild Bootstrap, Tamed at Last," STICERD - Distributional Analysis Research Programme Papers 58, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
  6. Donald W. K. Andrews & Marcelo J. Moreira & James H. Stock, 2006. "Optimal Two-Sided Invariant Similar Tests for Instrumental Variables Regression," Econometrica, Econometric Society, vol. 74(3), pages 715-752, 05.
  7. Russell Davidson & James G. MacKinnon, 2006. "Bootstrap Inference in a Linear Equation Estimated by Instrumental Variables," Working Papers 1024, Queen's University, Department of Economics.
  8. Davidson, R. & Mackinnon, J.G., 1996. "The Size Distorsion of Bootstrap Tests," G.R.E.Q.A.M. 96a15, Universite Aix-Marseille III.
  9. Gonçalves, Sílvia & Kilian, Lutz, 2002. "Bootstrapping autoregressions with conditional heteroskedasticity of unknown form," Working Paper Series 0196, European Central Bank.
  10. Marcelo J. Moreira, 2003. "A Conditional Likelihood Ratio Test for Structural Models," Econometrica, Econometric Society, vol. 71(4), pages 1027-1048, 07.
  11. Alfonso Flores-Lagunes, 2007. "Finite sample evidence of IV estimators under weak instruments," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(3), pages 677-694.
  12. Russell Davidson & Emmanuel Flachaire, 2001. "The wild bootstrap, tamed at last," LSE Research Online Documents on Economics 6560, London School of Economics and Political Science, LSE Library.
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Citations

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Cited by:
  1. Russell Davidson & James Mackinnon, 2009. "Bootstrap inference in a linear equation estimated by instrumental variables," Working Papers halshs-00442713, HAL.
  2. Lorenzo Camponovo & Taisuke Otsu, 2014. "Robustness of bootstrap in instrumental variable regression," STICERD - Econometrics Paper Series /2014/572, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
  3. Noud P.A. van Giersbergen, 2011. "Bootstrapping Subset Test Statistics in IV Regression," UvA-Econometrics Working Papers 11-08, Universiteit van Amsterdam, Dept. of Econometrics.
  4. Russell Davidson & James G. MacKinnon, 2014. "Bootstrap tests for overidentification in linear regression models," Working Papers 1318, Queen's University, Department of Economics.
  5. Simon A. Broda, 2014. "On Distributions of Ratios," Tinbergen Institute Discussion Papers 13-211/VI, Tinbergen Institute.
  6. James G. MacKinnon, 2012. "Thirty Years of Heteroskedasticity-Robust Inference," Working Papers 1268, Queen's University, Department of Economics.
  7. Patrick M. Kline & Andres Santos, 2011. "Higher Order Properties of the Wild Bootstrap Under Misspecification," NBER Working Papers 16793, National Bureau of Economic Research, Inc.
  8. Simon A. Broda & Raymond Kan, 2013. "On Distributions of Ratios," UvA-Econometrics Working Papers 13-10, Universiteit van Amsterdam, Dept. of Econometrics.
  9. Russell Davidson & James G. MacKinnon, 2011. "Confidence Sets Based on Inverting Anderson-Rubin Tests," Working Papers 1257, Queen's University, Department of Economics.
  10. Makram El-Shagi & Claus Michelsen & Sebastian Rosenschon, 2014. "Regulation, Innovation and Technology Diffusion: Evidence from Building Energy Efficiency Standards in Germany," Discussion Papers of DIW Berlin 1371, DIW Berlin, German Institute for Economic Research.
  11. Justine S. Hastings & Christopher A. Neilson & Seth D. Zimmerman, 2013. "Are Some Degrees Worth More than Others? Evidence from college admission cutoffs in Chile," NBER Working Papers 19241, National Bureau of Economic Research, Inc.

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