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Testing for Weak Identification in Possibly Nonlinear Models

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  • Barbara Rossi
  • Atsushi Inoue

Abstract

In this paper we propose a chi-square test for identification. Our proposed test statistic is based on the distance between two shrinkage extremum estimators. The two estimators converge in probability to the same limit when identification is strong, and their asymptotic distributions are different when identification is weak. The proposed test is consistent not only for the alternative hypothesis of no identification but also for the alternative of weak identification, which is confirmed by our Monte Carlo results. We apply the proposed technique to test whether the structural parameters of a representative Taylor-rule monetary policy reaction function are identified.

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Bibliographic Info

Paper provided by Duke University, Department of Economics in its series Working Papers with number 10-92.

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Length: 49
Date of creation: 2010
Date of revision:
Handle: RePEc:duk:dukeec:10-92

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Keywords: GMM; Shrinkage; Weak Identification;

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Citations

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Cited by:
  1. Dufour, Jean-Marie & Khalaf, Lynda & Kichian, Maral, 2013. "Identification-robust analysis of DSGE and structural macroeconomic models," Journal of Monetary Economics, Elsevier, vol. 60(3), pages 340-350.
  2. 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.
  3. Zisimos Koustas & Jean-François Lamarche, 2012. "Instrumental variable estimation of a nonlinear Taylor rule," Empirical Economics, Springer, vol. 42(1), pages 1-20, February.
  4. Xiaohong Chen & David T. Jacho-Chavez & Oliver Linton, 2012. "Averaging of moment condition estimators," CeMMAP working papers CWP26/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

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