论文标题
在事件研究中对不连续性的基于置换的测试
Permutation-based tests for discontinuities in event studies
论文作者
论文摘要
我们建议使用置换测试在已知的截止点处检测基本经济模型中的不连续性。相对于现有文献，我们表明该测试非常适合基于时间序列数据的事件研究。测试统计量衡量了截止两侧的两个局部样本中观察到的数据的经验分布函数之间的距离。临界值是通过标准置换算法计算的。在高级别的条件下，观察到的数据可以通过有条件自变量的集合来耦合，我们建立了置换测试的渐近有效性，从而允许局部样本的大小固定或生长至无穷大。在后一种情况下，我们还确定置换测试是一致的。我们证明，在填充渐近时间序列设置中，可以在广泛的问题中验证我们的高级状况，这证明使用置换测试来检测经济变量（例如波动性，交易活动和流动性）的跳跃。这些潜在的应用在经验案例研究中为正在进行的COVID-19大流行期间选定的FOMC公告提供了说明。
We propose using a permutation test to detect discontinuities in an underlying economic model at a known cutoff point. Relative to the existing literature, we show that this test is well suited for event studies based on time-series data. The test statistic measures the distance between the empirical distribution functions of observed data in two local subsamples on the two sides of the cutoff. Critical values are computed via a standard permutation algorithm. Under a high-level condition that the observed data can be coupled by a collection of conditionally independent variables, we establish the asymptotic validity of the permutation test, allowing the sizes of the local subsamples to be either be fixed or grow to infinity. In the latter case, we also establish that the permutation test is consistent. We demonstrate that our high-level condition can be verified in a broad range of problems in the infill asymptotic time-series setting, which justifies using the permutation test to detect jumps in economic variables such as volatility, trading activity, and liquidity. These potential applications are illustrated in an empirical case study for selected FOMC announcements during the ongoing COVID-19 pandemic.
