This study contributes to the growing research that uses the news-based measure of U.S. economic policy uncertainty (EPU) suggested in Baker et al. (2016) to predict economic variables out-of-sample. Using simple predictive regressions à la Goyal and Welch (2008), we evaluate the predictive power afforded by various nonlinear transformations of the U.S. EPU measure suggested in Baker et al. (2016) to predict excess returns on the S&P 500 index one-month ahead. Using data from $1985m1$ through $2020m12$, we find that not all EPU movements are equally important for obtaining point prediction improvements relative to the historical average benchmark at the population level. Particularly, we document that the one-year net EPU increase, defined as EPU increases beyond the peak over the last year, otherwise zero delivers the most consistent pattern of prediction improvements relative to the benchmark. Conversely, other nonlinear specifications as well as the linear models using log-EPU and the first difference of log-EPU do not deliver the same performance. Overall, this study documents that the predictive impact of the U.S. EPU index suggested in Baker et al. (2016) on equity premium is nonlinear in that EPU increases matter only to the extent that they exceed the maximum value over the last twelve months. In other words, there is evidence of threshold nonlinearity. The statistical predictive power afforded by the one-year net EPU increase also translates into economic gains.

这项研究有助于不断增长的研究，该研究使用 Baker 等人建议的基于新闻的美国经济政策不确定性 (EPU) 衡量标准。(2016) 预测样本外的经济变量。使用简单的预测回归 à la Goyal 和 Welch (2008)，我们评估了 Baker 等人建议的美国 EPU 度量的各种非线性变换所提供的预测能力。(2016) 预测未来一个月标准普尔 500 指数的超额回报。使用数据来自$\mathrm{1985年}米1$ 通过 $\mathrm{2020年}米12$，我们发现并非所有 EPU 运动对于获得相对于人口水平的历史平均基准的点预测改进都同样重要。特别是，我们记录了一年的净 EPU 增长，定义为 EPU 增长超过去年的峰值，否则零提供相对于基准的最一致的预测改进模式。相反，其他非线性规范以及使用 log-EPU 和 log-EPU 的一阶差分的线性模型不能提供相同的性能。总的来说，这项研究记录了 Baker 等人提出的美国 EPU 指数的预测影响。（2016 年）关于股权溢价是非线性的，因为 EPU 只会在超过过去 12 个月的最大值时才会增加物质。换句话说，有阈值非线性的证据。一年 EPU 净增长所提供的统计预测能力也转化为经济收益。