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Exactly distribution-free inference in instrumental variables regression with possibly weak instruments

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  • Andrews, Donald W.K.
  • Marmer, Vadim

Abstract

This paper introduces a rank-based test for the instrumental variables regression model that dominates the Anderson-Rubin test in terms of finite sample size and asymptotic power in certain circumstances. The test has correct size for any distribution of the errors with weak or strong instruments. The test has noticeably higher power than the Anderson-Rubin test when the error distribution has thick tails and comparable power otherwise. Like the Anderson-Rubin test, the rank tests considered here perform best, relative to other available tests, in exactly-identified models.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 142 (2008)
Issue (Month): 1 (January)
Pages: 183-200

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Handle: RePEc:eee:econom:v:142:y:2008:i:1:p:183-200

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Web page: http://www.elsevier.com/locate/jeconom

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  1. Joshua D. Angrist & William N. Evans, 1996. "Children and Their Parents' Labor Supply: Evidence from Exogenous Variation in Family Size," NBER Working Papers 5778, National Bureau of Economic Research, Inc.
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  6. Donald W.K. Andrews & Gustavo Soares, 2006. "Rank Tests for Instrumental Variables Regression with Weak Instruments," Cowles Foundation Discussion Papers 1564, Cowles Foundation for Research in Economics, Yale University.
  7. Donald W.K. Andrews & Marcelo Moreira & James H. Stock, 2004. "Optimal Invariant Similar Tests for Instrumental Variables Regression," NBER Technical Working Papers 0299, National Bureau of Economic Research, Inc.
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  10. Esther Duflo & Emmanuel Saez, 2002. "The Role of Information and Social Interactions in Retirement Plan Decisions: Evidence from a Randomized Experiment," NBER Working Papers 8885, National Bureau of Economic Research, Inc.
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  12. Douglas Staiger & James H. Stock, 1997. "Instrumental Variables Regression with Weak Instruments," Econometrica, Econometric Society, vol. 65(3), pages 557-586, May.
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Citations

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Cited by:
  1. Cattaneo, Matias D. & Crump, Richard K. & Jansson, Michael, 2012. "Optimal inference for instrumental variables regression with non-Gaussian errors," Journal of Econometrics, Elsevier, vol. 167(1), pages 1-15.
  2. Nick Bloom & Ben Eifert & Aprajit Mahajan & David McKenzie & John Roberts, 2010. "Does management matter?: evidence from India," LSE Research Online Documents on Economics 36366, London School of Economics and Political Science, LSE Library.
  3. Guggenberger, Patrik & Smith, Richard J., 2008. "Generalized empirical likelihood tests in time series models with potential identification failure," Journal of Econometrics, Elsevier, vol. 142(1), pages 134-161, January.
  4. Donald W.K. Andrews & Vadim Marmer, 2005. "Exactly Distribution-free Inference in Instrumental Variables Regression with Possibly Weak Instruments," Cowles Foundation Discussion Papers 1501, Cowles Foundation for Research in Economics, Yale University.
  5. Kazuhiko Hayakawa, 2006. "Efficient GMM Estimation of Dynamic Panel Data Models Where Large Heterogeneity May Be Present," Hi-Stat Discussion Paper Series d05-130, Institute of Economic Research, Hitotsubashi University.
  6. Aviv Nevo & Adam Rosen, 2008. "Identification with imperfect instruments," CeMMAP working papers CWP16/08, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  7. Leandro M. Magnusson & Sophocles Mavroeidis, 2011. "Identification Using Stability Restrictions," Working Papers 1116, Tulane University, Department of Economics.
  8. Donald W.K. Andrews & James H. Stock, 2005. "Inference with Weak Instruments," Cowles Foundation Discussion Papers 1530, Cowles Foundation for Research in Economics, Yale University.
  9. Elise Coudin & Jean-Marie Dufour, 2010. "Finite and Large Sample Distribution-Free Inference in Median Regressions with Instrumental Variables," Working Papers 2010-56, Centre de Recherche en Economie et Statistique.
  10. Guggenberger, Patrik & Ramalho, Joaquim J.S. & Smith, Richard J., 2012. "GEL statistics under weak identification," Journal of Econometrics, Elsevier, vol. 170(2), pages 331-349.

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