Hypothesis Testing in Econometrics
Hypothesis Testing in Econometrics
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DOI：
10.2139/ssrn.1477886
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发表时间：
2009-09
期刊：
影响因子：
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通讯作者：
Joseph P. Romano;A. Shaikh;Michael Wolf
中科院分区：
文献类型：
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作者：
Joseph P. Romano;A. Shaikh;Michael Wolf
本文回顾了对假设检验有用的重要概念和方法。首先，我们讨论内曼-皮尔逊框架。提出了各种优化方法，包括有限样本和大样本优化。然后，我们总结了一些最重要的方法，以及重采样方法，这是有用的临界值的设置。最后，我们考虑了多重测试的问题，这是近年来新兴的文献。在此过程中，我们结合了计量经济学文献中当前的一些例子。虽然包含了许多众所周知的成功解决方案的问题，但我们也解决了当前技术不容易处理的开放性问题，这些问题源于缺乏最优性或较差的渐近近似。
This article reviews important concepts and methods that are useful for hypothesis testing. First, we discuss the Neyman-Pearson framework. Various approaches to optimality are presented, including finite-sample and large-sample optimality. Then, we summarize some of the most important methods, as well as resampling methodology, which is useful to set critical values. Finally, we consider the problem of multiple testing, which has witnessed a burgeoning literature in recent years. Along the way, we incorporate some examples that are current in the econometrics literature. While many problems with well-known successful solutions are included, we also address open problems that are not easily handled with current technology, stemming from such issues as lack of optimality or poor asymptotic approximations.