The zero-information-limit condition and spurious inference in weakly identified models
AbstractThe fact that weak instruments lead to spurious inference is now widely recognized. In this paper we ask whether spurious inference occurs more generally in weakly identified models. To distinguish between models where spurious inference will occur from those where it does not, we introduce the Zero-Information-Limit-Condition (ZILC). When ZILC holds, the information or precision of parameter estimates is overestimated. We discuss how ZILC applies to models encountered in practice and show that spurious inference does occur when ZILC holds.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Bibliographic InfoArticle provided by Elsevier in its journal Journal of Econometrics.
Volume (Year): 138 (2007)
Issue (Month): 1 (May)
Contact details of provider:
Web page: http://www.elsevier.com/locate/jeconom
Other versions of this item:
- Charles Nelson & Richard Startz, 2004. "The Zero-Information-Limit Condition and Spurious Inference in Weakly Identified Models," Working Papers UWEC-2004-03-FC, University of Washington, Department of Economics.
- Charles Nelson & Richard Startz, 2007. "The Zero-Information-Limit-Condition and Spurious Inference in Weakly Identified Models," Working Papers UWEC-2006-07-P, University of Washington, Department of Economics.
You can help add them by filling out this form.
CitEc Project, subscribe to its RSS feed for this item.
- Ma, Jun & Nelson, Charles R., 2010.
"Valid Inference for a Class of Models Where Standard Inference Performs Poorly: Including Nonlinear Regression, ARMA, GARCH, and Unobserved Components,"
256, Institute for Advanced Studies.
- Jun Ma & Charles R. Nelson, 2008. "Valid Inference for a Class of Models Where Standard Inference Performs Poorly: Including Nonlinear Regression, ARMA, GARCH, and Unobserved Components," Working Papers UWEC-2008-06-R, University of Washington, Department of Economics, revised Sep 2008.
- Andrews, Donald W.K. & Cheng, Xu, 2013.
"Maximum likelihood estimation and uniform inference with sporadic identification failure,"
Journal of Econometrics,
Elsevier, vol. 173(1), pages 36-56.
- Donald W. K. Andrews & Xu Cheng, 2011. "Maximum Likelihood Estimation and Uniform Inference with Sporadic Identification Failure," Cowles Foundation Discussion Papers 1824R, Cowles Foundation for Research in Economics, Yale University, revised Oct 2012.
- 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.
- Soloschenko, Max & Weber, Enzo, 2012. "Trend-Cycle Interactions and the Subprime Crisis: Analysis of US and Canadian Output," University of Regensburg Working Papers in Business, Economics and Management Information Systems 470, University of Regensburg, Department of Economics.
- Morley, James & Piger, Jeremy, 2008. "Trend/cycle decomposition of regime-switching processes," Journal of Econometrics, Elsevier, vol. 146(2), pages 220-226, October.
- Guerron-Quintana, Pablo A. & Inoue, Atsushi & Kilian, Lutz, 2009.
"Frequentist Inference in Weakly Identified DSGE Models,"
CEPR Discussion Papers
7447, C.E.P.R. Discussion Papers.
- Pablo Guerron-Quintana & Atsushi Inoue & Lutz Kilian, 2009. "Frequentist inference in weakly identified DSGE models," Working Papers 09-13, Federal Reserve Bank of Philadelphia.
- Juan Urquiza, 2011. "Income Asymmetries and the Permanent Income Hypothesis," Documentos de Trabajo 409, Instituto de Economia. Pontificia Universidad Católica de Chile..
- Liu, Yan & Luger, Richard, 2009. "Efficient estimation of copula-GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2284-2297, April.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei).
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.