Forecasting Inflation in a Data-Rich Environment: The Benefits of Machine Learning Methods
数据丰富环境下的通胀预测：机器学习方法的益处
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…