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The Zero-Information-Limit Condition and Spurious Inference in Weakly Identified Models

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  • Charles Nelson
  • Richard Startz

Abstract

The 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.

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Bibliographic Info

Paper provided by University of Washington, Department of Economics in its series Working Papers with number UWEC-2004-03-FC.

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Date of creation: Feb 2004
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Publication status: Forthcoming in Journal of Econometrics
Handle: RePEc:udb:wpaper:uwec-2004-03-fc

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Cited by:
  1. 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.
  2. Morley, James & Piger, Jeremy, 2008. "Trend/cycle decomposition of regime-switching processes," Journal of Econometrics, Elsevier, vol. 146(2), pages 220-226, October.
  3. Soloschenko, Max & Weber, Enzo, 2014. "Capturing the Interaction of Trend, Cycle, Expectations and Risk Premia in the US Term Structure," University of Regensburg Working Papers in Business, Economics and Management Information Systems 475, University of Regensburg, Department of Economics.
  4. Kishor, N. Kundan & Marfatia, Hardik A., 2013. "The time-varying response of foreign stock markets to U.S. monetary policy surprises: Evidence from the Federal funds futures market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 24(C), pages 1-24.
  5. 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," Economics Series 256, Institute for Advanced Studies.
  6. 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.
  7. Liu, Yan & Luger, Richard, 2009. "Efficient estimation of copula-GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2284-2297, April.
  8. 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.
  9. Juan Urquiza, 2011. "Income Asymmetries and the Permanent Income Hypothesis," Documentos de Trabajo 409, Instituto de Economia. Pontificia Universidad Católica de Chile..
  10. 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.

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