This article considers predictive regressions in which a structural break is allowed on an unknown date. We establish novel testing procedures for asset return predictability using empirical likelihood (EL) methods based on weighted score equations. The theoretical results are useful in practice because our unified framework does not require distinguishing whether the predictor variables are stationary or non-stationary. Monte Carlo simulation studies show that the EL-based tests perform well in terms of size and power in finite samples. Finally, as an empirical analysis, we test the predictability of the monthly S&P 500 value-weighted log excess return using various predictor variables.

中文翻译：

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结构性断裂下资产回报可预测性的新检验

本文考虑了在未知日期允许结构中断的预测回归。我们使用基于加权得分方程的经验似然 (EL) 方法建立新的资产回报可预测性测试程序。理论结果在实践中很有用，因为我们的统一框架不需要区分预测变量是平稳的还是非平稳的。蒙特卡罗模拟研究表明，基于 EL 的测试在有限样本的大小和功效方面表现良好。最后，作为实证分析，我们使用各种预测变量测试每月标准普尔 500 价值加权对数超额回报的可预测性。