Forecasting Inflation in a Data-Rich Environment: The Benefits of Machine Learning Methods
利用机器学习方法和新数据集预测美国通胀，发现随机森林模型因变量选择和捕捉非线性关系而优于传统基准模型。
Inflation forecasting is an important but difficult task. Here, we explore advances in machine learning (ML) methods and the availability of new datasets to forecast U.S. inflation. Despite the skepticism in the previous literature, we show that ML models with a large number of covariates are systematically more accurate than the benchmarks. The ML method that deserves more attention is the random forest model, which dominates all other models. Its good performance is due not only to its specific method of variable selection but also the potential nonlinearities between past key macroeconomic variables and inflation. Supplementary materials for this article are available online.