论文标题
宏观经济作为随机森林
The Macroeconomy as a Random Forest
论文作者
论文摘要
我开发了宏观经济随机森林（MRF），这是一种适应规范机器学习（ML）工具的算法，以在线性宏方程式中灵活地模拟不断发展的参数。它的主要输出，广义时变参数（GTVPS）是一种多功能设备，嵌套了许多流行的非线性（阈值/开关，平滑过渡，结构断裂/更改），并允许复杂的新型。该方法可在众多替代方案上获得明显的预测收益，预测2008年的失业率急剧上升，并且在通货膨胀方面表现良好。与大多数基于ML的方法不同，MRF是可以直接解释的 - 通过其GTVP。例如，成功的失业率预测是由于前瞻性变量的影响（例如，期限差，住房开始）在每次衰退之前几乎翻了一番。有趣的是，菲利普斯曲线确实已经变平了，它的可能性是高度周期性的。
I develop Macroeconomic Random Forest (MRF), an algorithm adapting the canonical Machine Learning (ML) tool to flexibly model evolving parameters in a linear macro equation. Its main output, Generalized Time-Varying Parameters (GTVPs), is a versatile device nesting many popular nonlinearities (threshold/switching, smooth transition, structural breaks/change) and allowing for sophisticated new ones. The approach delivers clear forecasting gains over numerous alternatives, predicts the 2008 drastic rise in unemployment, and performs well for inflation. Unlike most ML-based methods, MRF is directly interpretable -- via its GTVPs. For instance, the successful unemployment forecast is due to the influence of forward-looking variables (e.g., term spreads, housing starts) nearly doubling before every recession. Interestingly, the Phillips curve has indeed flattened, and its might is highly cyclical.
