经济计量学中的自助法
Bootstrap Methods in Econometrics
摘要：
Bootstrap是一种通过重新采样数据或从数据估计的模型来估计量或检验统计量的分布的方法。在各种计量经济学应用中，自举法提供了统计分布的近似值，置信区间的覆盖概率，以及假设检验的拒绝概率，这些近似值比一阶渐近分布理论的近似值更准确。真实和标称覆盖或拒绝概率之间的差异的减小可以非常大。此外，引导提供了一种方法来进行推理，在某些设置中，获得分析分布近似是困难的或不可能的。本文解释了自助法在计量经济学中的有用性和局限性。介绍是非正式的和临时的。它提供了对引导程序如何工作的直观理解。数学细节可在引用的参考文献中获得。
英文摘要：
The bootstrap is a method for estimating the distribution of an estimator or test statistic by re-sampling the data or a model estimated from the data. Under conditions that hold in a wide variety of econometric applications, the bootstrap provides approximations to distributions of statistics, coverage probabilities of confidence intervals, and rejection probabilities of hypothesis tests that are more accurate than the approximations of first-order asymptotic distribution theory. The reductions in the differences between true and nominal coverage or rejection probabilities can be very large. In addition, the bootstrap provides a way to carry out inference in certain settings where obtaining analytic distributional approximations is difficult or impossible. This article explains the usefulness and limitations of the bootstrap in contexts of interest in econometrics. The presentation is informal and expository. It provides an intuitive understanding of how the bootstrap works. Mathematical details are available in references that are cited.
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