Testing for Predictability in panels with General Predictors
一般预测变量下的面板可预测性检验AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要明确以收益可预测性为研究问题，并通过面板截面变异构造检验，直接对应收益预测与面板数据方法。
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一般预测变量下的面板可预测性检验
- 研究问题
- 面对预测变量持续性及截面依赖带来的推断困难，如何利用面板数据检验收益可预测性，并兼顾检验功效与稳健性？
- 方法与思路
- 作者利用截面变异构造两项面板检验，使标准正态与卡方渐近推断覆盖预测变量平稳、中度非平稳、近非平稳及单位根情形。
- 材料陈述的贡献
- 摘要称，两项检验允许预测变量存在弱或强截面依赖，并以较简便的构造兼顾稳健性与功效，扩大标准渐近推断的适用范围。
- 阅读关注点
- 核对两项统计量的构造、面板维度的渐近设定，以及强误差截面依赖的具体限制和不同预测变量持续性下的功效表现。
- 阅读边界
- 摘要明确要求回归误差具有强截面依赖；稳健性与功效均为作者陈述，不能据此确认任意相关结构下的有效性或有限样本表现。
原文摘要
Summary The difficulty of predicting returns has recently motivated researchers to start looking for tests that are either more powerful or robust to more features of the data. Unfortunately, the way that these tests work typically involves trading robustness for power or vice versa. The current paper takes this as its starting point to develop a new panel‐based approach to predictability that is both robust and powerful. Specifically, while the panel route to increased power is not new, the way in which the cross‐section variation is exploited also to achieve robustness with respect to the predictor is. The result is two new tests that enable asymptotically standard normal and chi‐squared inference across a wide range of empirically relevant scenarios in which the predictor may be stationary, moderately non‐stationary, nearly non‐stationary, or indeed unit root non‐stationary. The type of cross‐section dependence that can be permitted in the predictor is also very general, and can be weak or strong, although we do require that the cross‐section dependence in the regression errors is of the strong form. What is more, this generality comes at no cost in terms of complicated test construction. The new tests are therefore very user‐friendly. Copyright © 2016 John Wiley & Sons, Ltd.