In this study, we nowcast quarter-over-quarter US GDP growth rates between 2000Q2 and 2018Q4 using tree-based ensemble machine learning models, namely, bagged decision trees, random forests, and stochastic gradient tree boosting. To solve the ragged edge problem and reduce the dimension of the data set, we adopt a dynamic factor model. Dynamic factors extracted from 10 groups of financial and macroeconomic variables are fed to machine learning models for nowcasting US GDP. Our results show that tree-based ensemble models usually outperform linear dynamic factor models. Factors obtained from real variables appear to be more influential in machine learning models. The impact of factors derived from financial and price variables can only become important in predicting GDP after the great financial crisis of 2008–9, reflecting the effect extra loose monetary policies implemented in the period following the crisis.

在这项研究中，我们使用基于树的集成机器学习模型，即袋装决策树、随机森林和随机梯度树提升，现在预测了 2000 年第二季度至 2018 年第四季度美国 GDP 的季度环比增长率。为了解决参差不齐的边缘问题并降低数据集的维数，我们采用了动态因子模型。从 10 组金融和宏观经济变量中提取的动态因素被输入机器学习模型，用于预测美国 GDP。我们的结果表明，基于树的集成模型通常优于线性动态因子模型。从真实变量中获得的因素似乎在机器学习模型中更具影响力。只有在 2008-9 年的大金融危机之后，来自金融和价格变量的因素的影响才对预测 GDP 变得重要，