Improved tests for stock return predictability

IF 0.8 4区 经济学 Q3 ECONOMICS Econometric Reviews Pub Date : 2023-07-14 DOI:10.1080/07474938.2023.2222634
David I. Harvey, S. Leybourne, A. Taylor
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引用次数: 1

Abstract

Abstract– Predictive regression methods are widely used to examine the predictability of (excess) stock returns by lagged financial variables characterized by unknown degrees of persistence and endogeneity. We develop a new hybrid test for predictability in these circumstances based on simple regression t-statistics. Where the predictor is endogenous, the optimal, but infeasible, test for predictability is based on the t-statistic on the lagged predictor in the basic predictive regression augmented with the current period innovation driving the predictor. We propose a feasible version of this augmented test, designed for the case where the predictor is an endogenous near-unit root process, using a GLS-based estimate of the innovation used in the infeasible test regression. The limiting null distribution of this statistic depends on both the endogeneity correlation parameter and the local-to-unity parameter characterizing the predictor. A method for obtaining asymptotic critical values is discussed and response surfaces are provided. We compare the asymptotic power properties of the feasible augmented test with those of a (non augmented) t-test recently considered in Harvey et al. and show that the augmented test is more powerful in the strongly persistent predictor case. We then propose using a weighted combination of the augmented statistic and the t-statistic of Harvey et al., where the weights are obtained using the p-values from a unit root test on the predictor. We find this can further improve asymptotic power in cases where the predictor has persistence at or close to that of a unit root process. Our final hybrid testing procedure then embeds the weighted statistic within a switching-based procedure which makes use of a standard predictive regression t-test, compared with standard normal critical values, when there is evidence for the predictor being weakly persistent. Monte Carlo simulations suggest that overall our new hybrid test displays superior finite sample performance to comparable extant tests.
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改进了股票收益可预测性的测试
摘要-预测回归方法被广泛用于检验具有未知持续程度和内生性的滞后金融变量对(超额)股票收益的可预测性。我们基于简单的回归t统计开发了一种新的混合测试,用于在这些情况下的可预测性。如果预测因子是内生的,那么最优但不可行的可预测性测试是基于基本预测回归中滞后预测因子的t统计量,以及驱动预测因子的当期创新。我们提出了这个增强测试的可行版本,设计用于预测器是内源性近单位根过程的情况,使用基于gls的估计在不可行的测试回归中使用的创新。该统计量的极限零分布取决于内生性相关参数和表征预测器的局部到单位参数。讨论了一种求渐近临界值的方法,并给出了响应曲面。我们比较了可行增广检验与Harvey等人最近考虑的(非增广)t检验的渐近幂性质,并表明增广检验在强持续性预测情况下更强大。然后,我们建议使用Harvey等人的增广统计量和t统计量的加权组合,其中权重是使用来自预测器的单位根检验的p值获得的。我们发现,当预测器的持久性等于或接近于单位根过程时,这可以进一步提高渐近能力。然后,我们最后的混合测试程序将加权统计嵌入到基于切换的程序中,该程序使用标准预测回归t检验,与标准正态临界值相比,当有证据表明预测器持久性较弱时。蒙特卡罗模拟表明,总的来说,我们的新混合测试比可比的现有测试显示出优越的有限样本性能。
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来源期刊
Econometric Reviews
Econometric Reviews 管理科学-数学跨学科应用
CiteScore
1.70
自引率
0.00%
发文量
27
审稿时长
>12 weeks
期刊介绍: Econometric Reviews is widely regarded as one of the top 5 core journals in econometrics. It probes the limits of econometric knowledge, featuring regular, state-of-the-art single blind refereed articles and book reviews. ER has been consistently the leader and innovator in its acclaimed retrospective and critical surveys and interchanges on current or developing topics. Special issues of the journal are developed by a world-renowned editorial board. These bring together leading experts from econometrics and beyond. Reviews of books and software are also within the scope of the journal. Its content is expressly intended to reach beyond econometrics and advanced empirical economics, to statistics and other social sciences.
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