We develop easy-to-implement tests for return predictability which, relative to extant tests in the literature, display attractive finite sample size control and power across a wide range of persistence and endogeneity levels for the predictor. Our approach is based on the standard regression $t$-ratio and a variant where the predictor is quasi-GLS (rather than OLS) demeaned. In the strongly persistent near-unit root environment, the limiting null distributions of these statistics depend on the endogeneity and local-to-unity parameters characterising the predictor. Analysis of the asymptotic local power functions of feasible implementations of these two tests, based on asymptotically conservative critical values, motivates a switching procedure between the two, employing the quasi-GLS demeaned variant unless the magnitude of the estimated endogeneity correlation parameter is small. Additionally, if the data suggests the predictor is weakly persistent, our approach switches to the standard $t$-ratio test with reference to standard normal critical values.

我们开发了易于实施的回报可预测性测试，相对于文献中的现有测试，该测试在预测器的广泛持久性和内生性水平上显示出有吸引力的有限样本大小控制和能力。我们的方法基于标准回归$吨$-ratio 和预测变量是准 GLS（而不是 OLS）贬低的变体。在强持久性近单位根环境中，这些统计数据的极限零分布取决于表征预测变量的内生性和局部到统一参数。对这两个测试的可行实现的渐近局部幂函数的分析，基于渐近保守的临界值，激发了两者之间的切换过程，使用准 GLS 贬值变体，除非估计的内生性相关参数的幅度很小。此外，如果数据表明预测器具有弱持久性，我们的方法将切换到标准$吨$- 参考标准正常临界值的比率测试。