The Zero-Information-Limit Condition and Spurious Inference
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. 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
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Bibliographic InfoPaper provided by Econometric Society in its series Econometric Society 2004 North American Winter Meetings with number 106.
Date of creation: 11 Aug 2004
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weak identification; ARMA;
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- C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General
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- Donald W.K. Andrews & Xu Cheng, 2011.
"GMM Estimation and Uniform Subvector Inference with Possible Identification Failure,"
Cowles Foundation Discussion Papers
1828, Cowles Foundation for Research in Economics, Yale University.
- Donald W.K. Andrews & Xu Cheng, 2011. "GMM Estimation and Uniform Subvector Inference with Possible Identification Failure," Cowles Foundation Discussion Papers 1828, Cowles Foundation for Research in Economics, Yale University, revised Jan 2013.
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