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Efficient GMM with nearly-weak instruments

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  • Bertille Antoine
  • Eric Renault

Abstract

This paper is in the line of the recent literature on weak instruments, which, following the seminal approach of Stock and Wright captures weak identification by drifting population moment conditions. In contrast with most of the existing literature, we do not specify a priori which parameters are strongly or weakly identified. We rather consider that weakness should be related specifically to instruments, or more generally to the moment conditions. In addition, we focus here on the case dubbed nearly-weak identification where the drifting DGP introduces a limit rank deficiency reached at a rate slower than root-T. This framework ensures the consistency of Generalized Method of Moments (GMM) estimators of all parameters, but at a rate possibly slower than usual. It also validates the GMM-LM test with standard formulas. We then propose a comparative study of the power of the LM test and its modified version, or K-test proposed by Kleibergen. Finally, after a well-suited rotation in the parameter space, we identify and estimate directions where root-T convergence is maintained. These results are all directly relevant for practical applications without requiring the knowledge or the estimation of the slower rate of convergence. Copyright (C) The Author(s). Journal compilation (C) Royal Economic Society 2009

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Bibliographic Info

Article provided by Royal Economic Society in its journal Econometrics Journal.

Volume (Year): 12 (2009)
Issue (Month): s1 (01)
Pages: S135-S171

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Handle: RePEc:ect:emjrnl:v:12:y:2009:i:s1:p:s135-s171

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Cited by:
  1. Donald W.K. Andrews & Xu Cheng, 2011. "GMM Estimation and Uniform Subvector Inference with Possible Identification Failure," Cowles Foundation Discussion Papers 1828, Cowles Foundation for Research in Economics, Yale University.
  2. Antoine, Bertille & Renault, Eric, 2012. "Efficient minimum distance estimation with multiple rates of convergence," Journal of Econometrics, Elsevier, vol. 170(2), pages 350-367.
  3. Donald W. K. Andrews & Xu Cheng, 2011. "Maximum Likelihood Estimation and Uniform Inference with Sporadic Identification Failure," Cowles Foundation Discussion Papers 1824, Cowles Foundation for Research in Economics, Yale University.
  4. Bertille Antoine & Otilia Boldea, 2014. "Efficient Inference with Time-Varying Identification Strength," Discussion Papers dp14-03, Department of Economics, Simon Fraser University.
  5. Xu Cheng, 2014. "Uniform Inference in Nonlinear Models with Mixed Identification Strength," PIER Working Paper Archive 14-018, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
  6. Rachida Ouysse, 2014. "On the performance of block-bootstrap continuously updated GMM for a class of non-linear conditional moment models," Computational Statistics, Springer, vol. 29(1), pages 233-261, February.

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