摘要翻译：
我们证明了非参数工具变量(NPIV)估计对错误规格高度敏感：对于一个广泛的估计类，对工具有效性的任意小偏差都会导致较大的渐近偏差。可以通过在估计中对结构函数施加严格的限制来缓解这个问题。然而，如果真正的功能不服从这些限制，那么强加这些限制就会带来偏见。因此，在对无效文书的敏感性和对施加过度限制的偏见之间存在着一种权衡。鉴于这种权衡，我们提出了一种NPIV模型估计的部分辨识方法。我们提供了一个点估计量，它使最坏情况下的渐近偏差和显式地说明某种程度的错误的误差界最小化。我们将我们的方法应用于Blundell等人的经验设置。（2007）和Horowitz(2011)估计形状不变恩格尔曲线。
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英文标题：
《Nonparametric Instrumental Variables Estimation Under Misspecification》
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作者：
Ben Deaner
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最新提交年份：
2019
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分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
  We show that nonparametric instrumental variables (NPIV) estimators are highly sensitive to misspecification: an arbitrarily small deviation from instrumental validity can lead to large asymptotic bias for a broad class of estimators. One can mitigate the problem by placing strong restrictions on the structural function in estimation. However, if the true function does not obey the restrictions then imposing them imparts bias. Therefore, there is a trade-off between the sensitivity to invalid instruments and bias from imposing excessive restrictions. In light of this trade-off we propose a partial identification approach to estimation in NPIV models. We provide a point estimator that minimizes the worst-case asymptotic bias and error-bounds that explicitly account for some degree of misspecification. We apply our methods to the empirical setting of Blundell et al. (2007) and Horowitz (2011) to estimate shape-invariant Engel curves.
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PDF链接：
https://arxiv.org/pdf/1901.01241