Testing for Weak Identification in Possibly Nonlinear Models

Barbara Rossi and Atsushi Inoue

No 10-92, Working Papers from Duke University, Department of Economics

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.

Keywords: GMM; Shrinkage; Weak Identification (search for similar items in EconPapers)
JEL-codes: C12 (search for similar items in EconPapers)
Pages: 49
Date: 2010
New Economics Papers: this item is included in nep-cba and nep-ecm
References: Add references at CitEc
Citations:

Downloads: (external link)
http://ssrn.com/abstract=1747169 main text

Related works:
Journal Article: Testing for weak identification in possibly nonlinear models (2011)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:duk:dukeec:10-92

Access Statistics for this paper

More papers in Working Papers from Duke University, Department of Economics Department of Economics Duke University 213 Social Sciences Building Box 90097 Durham, NC 27708-0097.
Bibliographic data for series maintained by Department of Economics Webmaster ().