Rank Tests for Instrumental Variables Regression with Weak Instruments
AbstractThis paper considers tests in an instrumental variables (IVs) regression model with IVs that may be weak. Tests that have near-optimal asymptotic power properties with Gaussian errors for weak and strong IVs have been determined in Andrews, Moreira, and Stock (2006a). In this paper, we seek tests that have near-optimal asymptotic power with Gaussian errors and improved power with non-Gaussian errors relative to existing tests. Tests with such properties are obtained by introducing rank tests that are analogous to the conditional likelihood ratio test of Moreira (2003). We also introduce a rank test that is analogous to the Lagrange multiplier test of Kleibergen (2002) and Moreira (2001).
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Bibliographic InfoPaper provided by Cowles Foundation for Research in Economics, Yale University in its series Cowles Foundation Discussion Papers with number 1564.
Length: 51 pages
Date of creation: Mar 2006
Date of revision:
Publication status: Published in Econometric Theory (2007), 23(6): 1033-1082
Note: CFP 1250.
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Postal: Cowles Foundation, Yale University, Box 208281, New Haven, CT 06520-8281 USA
Other versions of this item:
- Andrews, Donald W.K. & Soares, Gustavo, 2007. "Rank Tests For Instrumental Variables Regression With Weak Instruments," Econometric Theory, Cambridge University Press, vol. 23(06), pages 1033-1082, December.
- 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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- Leandro M. Magnusson, 2010.
"Inference in limited dependent variable models robust to weak identification,"
Royal Economic Society, vol. 13(3), pages S56-S79, October.
- Leandro M. Magnusson, 2008. "Inference in Limited Dependent Variable Models Robust to Weak Identification," Working Papers 0801, Tulane University, Department of Economics, revised Apr 2009.
- Andrews, Donald W.K. & Marmer, Vadim, 2008.
"Exactly distribution-free inference in instrumental variables regression with possibly weak instruments,"
Journal of Econometrics,
Elsevier, vol. 142(1), pages 183-200, January.
- 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.
- Donald W.K. Andrews & Patrik Guggenberger, 2007.
"Applications of Subsampling, Hybrid, and Size-Correction Methods,"
Cowles Foundation Discussion Papers
1608, Cowles Foundation for Research in Economics, Yale University.
- Andrews, Donald W.K. & Guggenberger, Patrik, 2010. "Applications of subsampling, hybrid, and size-correction methods," Journal of Econometrics, Elsevier, vol. 158(2), pages 285-305, October.
- Mathias D. Cattaneo & Richard K. Crump & Michael Jansson, 2007.
"Optimal Inference for Instrumental Variables Regression with non-Gaussian Errors,"
CREATES Research Papers
2007-11, School of Economics and Management, University of Aarhus.
- 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.
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