We utilize a battery of ensemble learning techniques [ensemble linear regression (LM), random forest], as well as two gradient boosting techniques [Gradient Boosting Decision Tree and Extreme Gradient Boosting (XGBoost)] to scrutinize the possibilities of enhancing the predictive accuracy of Economic Policy Uncertainty (EPU) index. Applied to a data-rich environment of the Newsbank media database, our LM and XGBoost assessments mostly outperform the other two ensemble learning procedures, as well as the original EPU index. Our LM and XGBoost estimates bring EPU closer to the stylized facts of uncertainty than other uncertainty estimates. LM and XGBoost indicators are more countercyclical and have more pronounced leading properties. We find that EPU is more strongly correlated to financial volatility measures than to consumers’ assessments of uncertainty. This corroborates that the media place a much higher weight on the financial sector than on the economic issues of consumers. Further on, we considerably widen the scope of search terms included in the calculation of EPU index. Using ensemble learning techniques on such a rich set of keywords, we mostly manage to outperform the standard EPU in terms of correlation with standard uncertainty proxies. We also find that the predictive accuracy of EPU index can be considerably increased using a more diversified set of uncertainty-related terms than the original EPU framework. Our estimates perform much better in a monthly setting (targeting the industrial production growth) than targeting quarterly GDP growth. This speaks in favor of uncertainty as a purely short-term phenomenon.

我们利用一系列集成学习技术 [集成线性回归 (LM)、随机森林] 以及两种梯度提升技术 [梯度提升决策树和极端梯度提升 (XGBoost)] 来审查提高预测准确性的可能性经济政策不确定性 (EPU) 指数。应用于 Newsbank 媒体数据库的数据丰富的环境，我们的 LM 和 XGBoost 评估大多优于其他两个集成学习程序以及原始 EPU 指数。与其他不确定性估计相比，我们的 LM 和 XGBoost 估计使 EPU 更接近不确定性的典型事实。LM 和 XGBoost 指标更具有反周期性，具有更明显的领先属性。我们发现与消费者对不确定性的评估相比，EPU 与金融波动性指标的相关性更强。这证实了媒体对金融部门的重视程度远高于对消费者经济问题的重视。此外，我们大大拓宽了 EPU 索引计算中包含的搜索词的范围。在如此丰富的关键字集上使用集成学习技术，我们在与标准不确定性代理的相关性方面大多设法胜过标准 EPU。我们还发现，与原始 EPU 框架相比，使用一组更多样化的不确定性相关术语可以显着提高 EPU 指数的预测准确性。我们的估计在月度设置（以工业生产增长为目标）比以季度 GDP 增长为目标要好得多。