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关注这篇论文的作者
Sam Astill
David I. Harvey

收益可预测性的 Bonferroni 型检验与初始条件

研究问题
当预测变量的初始值不可忽略或持续性较弱时,如何避免收益可预测性检验的显著性水平失真与功效损失?
方法与思路
作者结合 Bonferroni Q、Bonferroni t 和常规 t 检验的不同性质,提出两个对初始条件和持续性稳健的混合检验。
材料陈述的贡献
摘要称混合检验可渐近控制检验水平,并在不同初始值和持续性情形下具有良好功效;实证展示可预测性证据的变化。
阅读关注点
应核对混合规则、初始条件的度量、持续性参数范围、水平控制的条件,以及原始数据应用中的具体拒绝变化。
阅读边界
仅据所给 OpenAlex 版本摘要解读;摘要未说明混合检验公式和数值表现,更多拒绝不能直接解释为普遍优势或刊本结论。

原文摘要

We develop tests for predictability that are robust to both the magnitude of the initial condition and the degree of persistence of the predictor. While the popular Bonferroni Q test of Campbell and Yogo displays excellent power properties for strongly persistent predictors with an asymptotically negligible initial condition, it can suffer from severe size distortions and power losses when either the initial condition is asymptotically non-negligible or the predictor is weakly persistent. The Bonferroni t test of Elliott, and Stock, although displaying power well below that of the Bonferroni Q test for strongly persistent predictors with an asymptotically negligible initial condition, displays superior size control and power when the initial condition is asymptotically nonnegligible. In the case where the predictor is weakly persistent, a conventional regression t test comparing to standard normal quantiles is known to be asymptotically optimal under Gaussianity. Based on these properties, we propose two asymptotically size controlled hybrid tests that are functions of the Bonferroni Q, Bonferroni t, and conventional t tests. Our proposed hybrid tests exhibit very good power regardless of the magnitude of the initial condition or the persistence degree of the predictor. An empirical application to the data originally analyzed by Campbell and Yogo shows our new hybrid tests are much more likely to find evidence of predictability than the Bonferroni Q test when the initial condition of the predictor is estimated to be large in magnitude.

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