In this article, we compare three data-driven procedures to determine the bunching window in a Monte Carlo simulation of taxable income. Following the standard approach in the empirical bunching literature, we fit a flexible polynomial model to a simulated income distribution, excluding data in a range around a prespecified kink. First, we propose to implement methods for the estimation of structural breaks to determine a bunching regime around the kink. A second procedure is based on Cook’s distances aiming to identify outlier observations. Finally, we apply the iterative counterfactual procedure proposed by Bosch, Dekker, and Strohmaier which evaluates polynomial counterfactual models for all possible bunching windows. While our simulation results show that all three procedures are fairly accurate, the iterative counterfactual procedure is the preferred method to detect the bunching window when no prior information about the true size of the bunching window is available.

在本文中，我们比较了三种数据驱动的过程，以确定应税收入的蒙特卡洛模拟中的汇总窗口。遵循经验性聚类文献中的标准方法，我们将弹性多项式模型拟合到模拟的收入分配中，不包括预定弯折附近范围内的数据。首先，我们建议实施估计结构断裂的方法，以确定扭结附近的聚束状态。第二种方法是基于库克的距离，目的是识别离群值观测值。最后，我们应用了Bosch，Dekker和Strohmaier提出的迭代反事实程序，该程序针对所有可能的聚集窗口评估多项式反事实模型。虽然我们的仿真结果表明这三个过程都相当准确，