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The Zero-Information-Limit Condition and Spurious Inference

Author

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  • Richard Startz
  • Charles R. Nelson

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. Further, the numerator and denominator of the t-statistic will under certain circumstances be functionally related, not independent. We discuss how ZILC applies to models encountered in practice and show that spurious inference does occur when ZILC holds

Suggested Citation

  • Richard Startz & Charles R. Nelson, 2004. "The Zero-Information-Limit Condition and Spurious Inference," Econometric Society 2004 North American Winter Meetings 106, Econometric Society.
    • Handle: RePEc:ecm:nawm04:106
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    Cited by:

    1. Andrews, Donald W.K. & Cheng, Xu, 2014. "Gmm Estimation And Uniform Subvector Inference With Possible Identification Failure," Econometric Theory, Cambridge University Press, vol. 30(2), pages 287-333, April.

    More about this item

    Keywords

    weak identification; ARMA;

    JEL classification:

    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General

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