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Testing for weak identification in possibly nonlinear models

Listed author(s):
  • Inoue, Atsushi
  • Rossi, Barbara

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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File URL: http://www.sciencedirect.com/science/article/pii/S0304-4076(10)00257-5
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Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 161 (2011)
Issue (Month): 2 (April)
Pages: 246-261

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Handle: RePEc:eee:econom:v:161:y:2011:i:2:p:246-261
Contact details of provider: Web page: http://www.elsevier.com/locate/jeconom

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