The macroeconomy as a random forest
开发了宏观经济随机森林算法，用于灵活建模线性宏观方程中的时变参数，在预测失业和通胀方面优于多种方法，并揭示了菲利普斯曲线平坦化且具有强周期性的特征。
Summary I develop the 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, and 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 and housing starts) nearly doubling before every recession. Interestingly, the Phillips curve has indeed flattened, and its might is highly cyclical.