摘要翻译:
我们在线性IV模型中提出了弱辨识的概念,其中仪器的数量可以以相同的速度增长,或者比样本量慢。我们提出了经典弱辨识的一个jackknifed版本-鲁棒Anderson-Rubin(AR)检验统计量。基于jackknifed AR的大样本推理在异方差和弱辨识的情况下是有效的。这个统计量的可行版本使用了一个新的方差估计量。该试验具有均匀正确的尺寸和良好的功率性能。我们还开发了一个基于JIVE的弱辨识预检验,该预检验与Wald检验的大小性质有关。这种新的前测在异方差条件下是有效的,并且有许多仪器。
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英文标题:
《Inference with Many Weak Instruments》
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作者:
Anna Mikusheva and Liyang Sun
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最新提交年份:
2021
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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 develop a concept of weak identification in linear IV models in which the number of instruments can grow at the same rate or slower than the sample size. We propose a jackknifed version of the classical weak identification-robust Anderson-Rubin (AR) test statistic. Large-sample inference based on the jackknifed AR is valid under heteroscedasticity and weak identification. The feasible version of this statistic uses a novel variance estimator. The test has uniformly correct size and good power properties. We also develop a pre-test for weak identification that is related to the size property of a Wald test based on the Jackknife Instrumental Variable Estimator (JIVE). This new pre-test is valid under heteroscedasticity and with many instruments.
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PDF链接:
https://arxiv.org/pdf/2004.12445