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Quantifying the data-dredging bias in structural break tests
Statistical Papers ( IF 1.3 ) Pub Date : 2021-04-16 , DOI: 10.1007/s00362-021-01233-4
Yannick Hoga

Structural break tests are often applied as a pre-step to ensure the validity of subsequent statistical analyses. Without any a priori knowledge of the type of breaks to expect, eye-balling the data can indicate changes in some parameter, e.g., the mean. This, however, can distort the result of a structural break test for that parameter, because the data themselves suggested the hypothesis. In this paper, we formalize the eye-balling procedure and theoretically derive the implied size distortion of the structural break test. We also show that eye-balling a stretch of historical data for possible changes in a parameter does not invalidate the subsequent procedure that monitors for structural change in new incoming observations. An empirical application to Bitcoin returns shows that taking into account the data-dredging bias, which is incurred by looking at the data, can lead to different test decisions.



中文翻译:

量化结构破坏测试中的数据挖掘偏差

结构性断裂试验通常被用作确保后续统计分析有效性的预备步骤。如果没有对中断类型的任何先验知识,眼球数据可以指示某些参数(例如平均值)的变化。但是,这可能会使该参数的结构破坏测试的结果失真,因为数据本身就提出了假设。在本文中,我们对目测程序进行了形式化,并从理论上推导了结构断裂试验的隐含尺寸畸变。我们还表明,将大量历史数据用于参数可能的变化不会使后续过程无效,该过程将监视新输入观测值中的结构变化。对比特币收益的经验应用表明,考虑到数据挖掘的偏见,

更新日期:2021-04-18
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