ESTIMATION AND INFERENCE FOR MOMENTS OF RATIOS WITH ROBUSTNESS AGAINST LARGE TRIMMING BIAS
针对估计比率矩时因大修剪观测值产生的偏差，提出一种基于分布平滑性的偏差校正方法，并配套推断工具，模拟和实证均显示其有效性。
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.