The Zero-Information-Limit Condition and Spurious Inference in Weakly Identified Models
The Zero-Information-Limit Condition and Spurious Inference in Weakly Identified Models
复制标题
弱识别模型中的零信息极限条件和虚假推理
DOI：
10.2139/ssrn.497282
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发表时间：
2004
期刊：
影响因子：
--
通讯作者：
R. Startz
中科院分区：
文献类型：
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作者：
C. Nelson;R. Startz
弱的仪器会导致虚假的推论，这一事实现在已被广泛认识。在本文中，我们问是否虚假的推理更普遍地发生在弱识别模型。为了区分哪些模型会出现虚假推断，哪些不会出现虚假推断，我们引入了零信息限制条件（ZILC）。当ZILC成立时，参数估计的信息或精度被高估。此外，在某些情况下，t统计量的分子和分母在功能上是相关的，而不是独立的。我们讨论了如何ZILC适用于在实践中遇到的模型，并表明，虚假的推理时，ZILC举行
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