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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.
(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • 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.
  • Handle: RePEc:udb:wpaper:uwec-2006-07-p
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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. Liu, Yan & Luger, Richard, 2009. "Efficient estimation of copula-GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2284-2297, April.
    5. Cogley, Timothy & Startz, Richard, 2012. "Robust Estimation of ARMA Models with Near Root Cancellation," University of California at Santa Barbara, Economics Working Paper Series qt0cw056qz, Department of Economics, UC Santa Barbara.
    6. Juan Urquiza, 2011. "Income Asymmetries and the Permanent Income Hypothesis," Documentos de Trabajo 409, Instituto de Economia. Pontificia Universidad Católica de Chile..
    7. Christian Murray & Juan Urquiza, 2017. "Do Estimated Taylor Rules Suffer from Weak Identification?," Working Papers 2017-274-09, Department of Economics, University of Houston.
    8. Daisuke Nagakura & Masahito Kobayashi, 2009. "Testing The Sequential Logit Model Against The Nested Logit Model," The Japanese Economic Review, Japanese Economic Association, vol. 60(3), pages 345-361.
    9. 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.
    10. Trypsteen, Steven, 2017. "The growth-volatility nexus: New evidence from an augmented GARCH-M model," Economic Modelling, Elsevier, vol. 63(C), pages 15-25.
    11. 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.
    12. Cheng, Xu, 2015. "Robust inference in nonlinear models with mixed identification strength," Journal of Econometrics, Elsevier, vol. 189(1), pages 207-228.
    13. Morley, James & Piger, Jeremy, 2008. "Trend/cycle decomposition of regime-switching processes," Journal of Econometrics, Elsevier, vol. 146(2), pages 220-226, October.
    14. 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.
    15. Steven Trypsteen, 2014. "The Importance of a Time-Varying Variance and Cross-Country Interactions in Forecast Models," Discussion Papers 2014/15, University of Nottingham, Centre for Finance, Credit and Macroeconomics (CFCM).
    16. Ma, Jun & Wohar, Mark E., 2014. "Determining what drives stock returns: Proper inference is crucial: Evidence from the UK," International Review of Economics & Finance, Elsevier, vol. 33(C), pages 371-390.
    17. 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.
    18. Islas C., Alejandro & Cortez, Willy Walter, 2013. "An assessment of the dynamics between the permanent and transitory components of Mexico's output and unemployment," Revista CEPAL, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL), December.

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