Researchers often trim observations with small values of the denominator A when they estimate moments of the form $\mathbb {E}[B/A]$ . Large trimming is common in practice to reduce variance, but it incurs a large bias. This paper provides a novel method of correcting the large trimming bias. If a researcher is willing to assume that the joint distribution between A and B is smooth, then the trimming bias may be estimated well. Along with the proposed bias correction method, we also develop an inference method. Practical advantages of the proposed method are demonstrated through simulation studies, where the data generating process entails a heavy-tailed distribution of $B/A$ . Applying the proposed method to the Compustat database, we analyze the history of external financial dependence of U.S. manufacturing firms for years 2000–2010.

中文翻译：

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对大修整偏差具有鲁棒性的比率矩的估计和推断

研究人员经常用较小的分母值修剪观察结果一种当他们估计形式的时刻时$\mathbb {E}[B/A]$. 大修边在实践中很常见以减少方差，但它会产生很大的偏差。本文提供了一种修正大微调偏差的新方法。如果研究人员愿意假设一种和乙是平滑的，那么可以很好地估计修剪偏差。除了提出的偏差校正方法外，我们还开发了一种推理方法。通过模拟研究证明了所提出方法的实际优势，其中数据生成过程需要重尾分布$B/A$. 将所提出的方法应用于 Compustat 数据库，我们分析了 2000 年至 2010 年美国制造公司的外部财务依赖历史。