Predictive regression under various degrees of persistence and robust long-horizon regression
Predictive regression under various degrees of persistence and robust long-horizon regression
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DOI：
10.1016/j.jeconom.2013.04.011
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发表时间：
2013-12
影响因子：
6.3
通讯作者：
P. Phillips;Ji Hyung Lee
中科院分区：
文献类型：
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作者：
P. Phillips;Ji Hyung Lee
本文提出了一种新的推理过程的长期预测回归与持久的回归，允许自回归根位于一个广泛的附近的单位。当回归变量是持续的传统测试的无效性，导致了大量的文献处理与本地到单位回归预测回归推理。Magdalinos和菲利普斯（2009 b）最近开发了一个新的扩展IV程序（IVX）框架，该框架可以对更广泛的持久回归变量进行稳健的卡方检验。我们扩展这个强大的过程，甚至更广泛的参数空间附近的团结和应用的方法，长期预测回归。现有的方法在这个模型中，依赖于模拟的临界值，在当地的单位条件下，通过反演测试，不能很容易地扩展到标量回归的情况下，或更广泛的自回归参数化。相比之下，这里开发的方法导致标准卡方检验，允许多元回归，并包括预测过程，其根可能位于一个广泛的附近的单位。因此，它们在预测回归中有许多潜在的应用。除了无可预测性的零假设下的渐近性，本文调查的替代品下的有效性，显示如何在回归中的平衡，可以通过使用本地化系数和开发本地渐近功率属性下，这样的替代品。这些结果有助于解释在建立股票收益的可预测性时遇到的一些经验困难。
The paper proposes a novel inference procedure for long-horizon predictive regression with persistent regressors, allowing the autoregressive roots to lie in a wide vicinity of unity. The invalidity of conventional tests when regressors are persistent has led to a large literature dealing with inference in predictive regressions with local to unity regressors. Magdalinos and Phillips (2009b) recently developed a new framework of extended IV procedures (IVX) that enables robust chi-square testing for a wider class of persistent regressors. We extend this robust procedure to an even wider parameter space in the vicinity of unity and apply the methods to long-horizon predictive regression. Existing methods in this model, which rely on simulated critical values by inverting tests under local to unity conditions, cannot be easily extended beyond the scalar regressor case or to wider autoregressive parametrizations. In contrast, the methods developed here lead to standard chi-square tests, allow for multivariate regressors, and include predictive processes whose roots may lie in a wide vicinity of unity. As such they have many potential applications in predictive regression. In addition to asymptotics under the null hypothesis of no predictability, the paper investigates validity under the alternative, showing how balance in the regression may be achieved through the use of localizing coefficients and developing local asymptotic power properties under such alternatives. These results help to explain some of the empirical difficulties that have been encountered in establishing predictability of stock returns.